Analysis system and analysis method

Abstract

To provide an analysis system and an analysis method for distinguishing objects in a space into human and non-human objects.SOLUTION: There is provided an analysis system including a range measuring sensor that measures distances to objects in a predetermined space at predetermined measurement time intervals and outputs three-dimensional point cloud data, and a processing unit. The processing unit executes: an initial background extraction process S1 of extracting a stationary object based on the point cloud data output from the range measuring sensor within a predetermined initial background extraction period and registering, as an initial background, each cell that constitutes each stationary object in a cell arrangement obtained by viewing the predetermined space from above and subdividing it into a lattice form; a moving object extraction process S2 of extracting a moving object by subtracting the initial background from the point cloud data output from the range measuring sensor within a predetermined observation period after the predetermined initial background extraction period; and a movable stationary object extraction process S3 of extracting a movable stationary object from the extracted moving object and redefining each cell constituting each movable stationary object in the cell arrangement as a post-defined background.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to an analysis system and an analysis method. [Background technology]

[0002] Conventionally, there is a technique for creating an environmental map using a sensor (for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2019-67001 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, conventional techniques are only capable of grasping the positions and shapes of objects in a space, and are not capable of distinguishing between people and non-people objects in a space.

[0005] An object of the present invention is to provide an analysis system and an analysis method that make it possible to distinguish between objects in a space, such as humans and non-human objects. [Means for solving the problem]

[0006] The analysis system of the present invention comprises: a range sensor that measures the distance to an object in a predetermined space at each measurement time within a predetermined measurement time interval and outputs three-dimensional point cloud data; a processing unit; An analysis system comprising: The processing unit an initial background extraction process for extracting one or more stationary objects based on the point cloud data output from the range measurement sensor at each of the measurement times within a predetermined initial background extraction period, and registering, as an initial background, each cell constituting each of the stationary objects in a cell array defined by overlooking the predetermined space and subdividing it into a grid; a moving object extraction process for extracting one or more moving objects by subtracting the initial background from the point cloud data output from the range measurement sensor at the measurement time within a predetermined observation period after the predetermined initial background extraction period; a movable stationary object extraction process for extracting one or more movable stationary objects from the one or more moving objects and redefining each cell constituting each of the movable stationary objects in the cell array as a post-defined background; The device is configured to:

[0007] In the analysis system of the present invention, The processing unit, in the initial background extraction process, an object extraction process for extracting one or more objects based on the point cloud data output from the range measurement sensor at the measurement time within the predetermined initial background extraction period; a high-position stationary object extraction process for extracting one or more first objects, at least a portion of which is located at a height position equal to or higher than a predetermined height, as stationary objects from among the one or more objects extracted by the object extraction process, and registering each cell constituting each of the stationary objects in the cell array as the initial background; a linear stationary object extraction process that calculates a first linearity evaluation value representing the degree of linearity of one or more second objects other than the one or more first objects extracted by the high-position stationary object extraction process among the one or more objects extracted by the object extraction process, extracts one or more second objects whose first linearity evaluation value exceeds a predetermined first linearity evaluation value threshold as a stationary object, and registers each cell constituting each of the stationary objects in the cell array as the initial background; It is preferable that the device is configured to perform the following.

[0008] In the analysis system of the present invention, The processing unit, in the movable stationary object extraction process, When a first moving object among the one or more moving objects transitions from a sitting state to a standing state, and then a new moving object is extracted around the first moving object by the moving object extraction process, and then, within a predetermined new moving object monitoring time from the time when the following formula (5) is first satisfied, if the time during which the formula (5) is continuously satisfied exceeds a predetermined duration threshold, the new moving object is extracted as a movable stationary object, and each cell constituting the movable stationary object in the cell array is redefined as a post-defined background, an interlocking movable stationary object extraction process It is preferable that the device is configured to perform the following.

number

[0009] In the analysis system of the present invention, a linear-type movable stationary object extraction process in which, when an obstructed area appears in the initial background at the same location over a predetermined obstructed area monitoring time, a second moving object located between the range measurement sensor and the obstructed area is identified from among the one or more moving objects, a second linearity evaluation value representing the degree of linearity of the object is calculated for the second moving object, and when the second linearity evaluation value exceeds a predetermined second linearity evaluation value threshold, the second moving object is extracted as a movable stationary object, and each cell constituting the movable stationary object in the cell array is redefined as a later-defined background; It is preferable that the device is configured to perform the following.

[0010] In the analysis system of the present invention, The processing unit, in the movable stationary object extraction process, When the highest point of a third moving object among the one or more moving objects is located at approximately the same position over a predetermined highest point monitoring time, the third moving object is extracted as a movable stationary object, and each cell constituting the movable stationary object in the cell array is redefined as a post-defined background. It is preferable that the device is configured to perform the following.

[0011] In the analysis system of the present invention, The processing unit, in the movable stationary object extraction process, a voxel number slight variation type movable stationary object extraction process, in which, when a voxel number variation evaluation value representing the degree of variation in the number of voxels constituting the fourth moving object within a predetermined voxel number monitoring time is less than a predetermined voxel number variation evaluation value threshold, the fourth moving object is extracted as a movable stationary object, and each cell constituting the movable stationary object in the cell array is redefined as a post-defined background; It is preferable that the device is configured to perform the following.

[0012] The analysis method of the present invention includes: a range sensor that measures the distance to an object in a predetermined space at each measurement time within a predetermined measurement time interval and outputs three-dimensional point cloud data; a processing unit; An analysis method using an analysis system comprising: an initial background extraction step in which the processing unit extracts one or more stationary objects based on the point cloud data output from the range measurement sensor at each of the measurement times within a predetermined initial background extraction period, and registers, as an initial background, each cell constituting each of the stationary objects in a cell array defined by overlooking the predetermined space and subdividing it into a grid; a moving object extraction step in which the processing unit extracts one or more moving objects by subtracting the initial background from the point cloud data output from the range measurement sensor at the measurement time within a predetermined observation period after the predetermined initial background extraction period; a movable stationary object extraction step in which the processing unit extracts one or more movable stationary objects from the one or more moving objects, and redefines each cell constituting each of the movable stationary objects in the cell array as a post-defined background; Includes. [Effects of the Invention]

[0013] According to the present invention, it is possible to provide an analysis system and an analysis method that make it possible to distinguish between objects in a space as humans and non-human objects. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a schematic diagram illustrating an analysis system according to an embodiment of the present invention. [Figure 2] 1 is a schematic flowchart of an analysis method according to an embodiment of the present invention. [Figure 3] 1 is a diagram for explaining types of objects; [Figure 4] 1 is a diagram for explaining an example of a predetermined space to be analyzed and an example of a cell array; [Figure 5] 5 is a diagram for explaining analysis data in a state where point cloud data of the predetermined space in FIG. 4 at a certain measurement time within a predetermined initial background extraction period is stored in a cell array. [Figure 6] 6 is a diagram for explaining analysis data obtained after performing an initial background extraction process on the analysis data of FIG. 5. [Figure 7] 5 is a diagram for explaining analysis data obtained after performing a moving object extraction process on point cloud data in a predetermined space in FIG. 4 at a certain measurement time within a predetermined observation period. [Figure 8] 10 is a diagram for explaining an interlocking movable stationary object extraction process; [Figure 9] 10 is a diagram for explaining a linear movable stationary object extraction process; [Figure 10] 10 is a diagram for explaining a linear movable stationary object extraction process; [Figure 11]10 is a diagram for explaining a voxel number slight variation type movable stationary object extraction process. [Figure 12] 10 is a diagram for explaining a voxel number slight variation type movable stationary object extraction process. [Figure 13] 7 is a diagram for explaining analysis data obtained after a movable object extraction process is performed on the analysis data of FIG. 6. [Figure 14] 1 is a diagram illustrating an example of an environmental map. DETAILED DESCRIPTION OF THE INVENTION

[0015] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, an analysis system and an analysis method according to an embodiment of the present invention will be described with reference to the accompanying drawings. In each drawing, the same components are denoted by the same reference numerals.

[0016] [Analysis System 1] First, an analysis system 1 according to one embodiment of the present invention, which can use the analysis method of the present invention, will be described with reference to Fig. 1 and Fig. 4. Fig. 1 shows a schematic configuration of the analysis system 1. The analysis system 1 is configured to analyze a predetermined space S (Fig. 4). As shown in Fig. 1, the analysis system 1 includes a range sensor 2 and an analysis device 3. Fig. 4 shows an overhead view of an example of the predetermined space S. The predetermined space S to be analyzed by the analysis system 1 is preferably an indoor space such as an office space or a school space. Specifically, the analysis system 1 is configured to distinguish between humans and non-human objects among objects in a predetermined space S, and analyze how people use the predetermined space S. Examples of users of the analysis system 1 include analysts, owners, and users of the predetermined space S.

[0017] Here, with reference to FIGS. 3 and 4, classification of objects in a predetermined space S will be described. As illustrated in FIG. 4, various objects O (Oa to Og) exist in the predetermined space S. As illustrated in FIG. 3, the objects O can be classified into stationary objects SO and moving objects MO. Stationary objects SO refer to objects that do not move at all during the measurement period of the range sensor 2, and examples thereof include walls, doors, and desks. In the example of FIG. 4, of the objects Oa to Og in the predetermined space S, objects Oa to Oc are stationary objects SO, and of these, objects Oa to Ob are walls and object Oc is a desk. Moving objects MO refer to objects that move at least temporarily during the measurement period of the range sensor 2. Moving objects MO can be classified into a person P and a movable stationary object MSO. A movable stationary object MSO refers to an object that moves due to interaction with a person P during the measurement period of the range sensor 2, and examples thereof include a chair, desk, whiteboard, potted plant, trash can, etc. 4, of the objects Oa to Og in the predetermined space S, the objects Od to ​​Og are moving objects, and among them, the object Oe is a person P, and the objects Od, Of, and Og are movable stationary objects MSO. Of the objects Od, Of, and Og as the movable stationary objects MSO, the object Od is a chair, the object Of is a potted plant, and the object Og is a trash can. The analysis system 1 is configured to distinguish a person P in a predetermined space S from stationary objects SO and movable stationary objects MSO, which are objects other than a person.

[0018] An xyz Cartesian coordinate system is defined in a predetermined space S. As shown in Fig. 4, the x-axis and y-axis are parallel to the horizontal direction, and the z-axis is parallel to the height direction. As will be described later, the analysis device 3 of the analysis system 1 analyzes a predetermined space S using a two-dimensional cell array CA. As shown in FIG. 4, the cell array CA is defined by subdividing the predetermined space S into a grid-like structure from a bird's-eye view. The cell array CA is parallel to the xy plane. The cells C of the cell array CA are arranged along the x-axis and the y-axis. Each cell C of the cell array CA forms a square. The length of one side of a cell C is preferably 13 cm or less, and more preferably 11 cm or less, from the viewpoint of preventing multiple objects O from being entirely contained within a single cell C. Furthermore, the length of one side of a cell C is preferably 7 cm or more, and more preferably 9 cm or more, from the viewpoint of preventing an increase in the processing load of the analysis device 3. The length of one side of a cell C is preferably 10 cm, for example. The coordinates of each cell C are expressed as (pq) in an xy Cartesian coordinate system, where p and q are preferably integers.

[0019] (Surface Sensor 2) The range sensor 2 is configured to measure the distance to an object O in a predetermined space S at each measurement time every predetermined measurement time interval (e.g., 0.1 seconds) over a predetermined measurement period and output three-dimensional point cloud data. The range sensor 2 is preferably configured, for example, as a 3D-LiDAR (3D Laser Imaging Detection and Ranging) or a 3D-LRF (3D Laser Range Finder) configured to measure the distance to an object O in the predetermined space S every predetermined measurement time interval using a laser and output three-dimensional point cloud data. Each point in the three-dimensional point cloud data is represented by an xyz coordinate system. Since the range sensor 2 is configured to output three-dimensional point cloud data, it can include height information and has fewer blind spots than if it were configured to output two-dimensional point cloud data, enabling more accurate analysis of the position, shape, and number of objects O. In addition, range sensors (such as 3D-LiDAR) configured to output three-dimensional point cloud data generally have a wider measurement range than range sensors (such as 2D-LiDAR) configured to output two-dimensional point cloud data, which also has the advantage of allowing the number of range sensors to be reduced. The number of range sensors 2 is preferably one, but may be multiple. When multiple range sensors 2 are used, the analysis device 3 may use point cloud data obtained by combining point cloud data from the multiple range sensors 2. The length of the predetermined measurement period is, for example, 0.5 to 5 hours. The range measurement sensor 2 is installed so that its detection range includes the entire predetermined space S. The range sensor 2 is preferably installed at a height slightly higher than the person P in the predetermined space S. For example, the range sensor 2 is preferably installed at a height of 1.75 to 1.90 m from the ground, and more preferably at a height of 1.80 to 1.85 m from the ground. The point cloud data obtained by the range sensor 2 is output to the analysis device 3. The range sensor 2 transmits the point cloud data to the analysis device 3 in real time via communication (wired communication and / or wireless communication) at each measurement time for each predetermined measurement time interval.

[0020] (Analysis device 3) 1, the analysis device 3 has a processing unit 31, a communication unit 32, a storage unit 33, an input unit 34, and a display unit 35. The analysis device 3 can be configured from any computer, such as a personal computer, a tablet terminal, a mobile terminal, or a dedicated device. The analysis device 3 may be configured from one computer or multiple computers.

[0021] The processing unit 31 is composed of, for example, one or more CPUs, and executes programs such as an analysis program stored in the storage unit 33 to perform processes such as an initial background extraction process, a moving object extraction process, a movable stationary object extraction process, and an environmental map creation process, which will be described later, while controlling the entire analysis device 3, including the communication unit 32, the storage unit 33, the input unit 34, and the display unit 35. Details of the processes performed by the processing unit 31 will be described later.

[0022] The communication unit 32 is configured to perform communication (wired communication and / or wireless communication) with the range measurement sensor 2. The analysis device 3 receives point cloud data from the range measurement sensor 2 via the communication unit 32.

[0023] The memory unit 33 is composed of, for example, one or more ROMs, one or more RAMs, and / or external storage devices (USB, SD card, etc.), and stores various information such as programs such as analysis programs to be executed by the processing unit 31, point cloud data from the range sensor 2, and calculation results of the processing unit 31.

[0024] The input unit 34 is composed of, for example, a keyboard, a mouse, and / or push buttons, and receives input from the user. The display unit 35 is configured by, for example, a liquid crystal panel, and displays various information such as an environmental map (FIG. 14) described later. The input unit 34 and the display unit 35 may form a touch panel. The analysis device 3 does not necessarily have to include the input unit 34 and / or the display unit 35.

[0025] [Analysis method] Next, an analysis method according to one embodiment of the present invention will be described with reference to Fig. 2 and Figs. 4 to 14. Fig. 2 is a schematic flowchart of the analysis method according to one embodiment of the present invention. Below, a case where the analysis system 1 shown in Fig. 1 is used will be described, but the analysis method of the present invention can also be implemented using an analysis system 1 different from the example shown in Fig. 1. The analysis method according to this embodiment includes an initial background extraction step S1, a moving object extraction step S2, a movable and stationary object extraction step S3, and an environmental map creation step S4. These steps S1 to S4 are performed by the processing unit 31 of the analysis device 3 by executing an analysis program stored in the storage unit 33. The range sensor 2 transmits point cloud data to the analysis device 3 in real time via communication (wired communication and / or wireless communication) at each measurement time for each predetermined measurement time interval, and the analysis device 3 performs steps S1 to S3 in real time during that time. Each step will be explained below.

[0026] -Initial background extraction step (S1)- First, in an initial background extraction step S1, the processing unit 31 performs an initial background extraction process in which it extracts one or more stationary objects SO based on the point cloud data output from the range measurement sensor 2 at each measurement time for each predetermined measurement time interval within a predetermined initial background extraction period, and registers each cell C constituting each stationary object SO in the cell array CA as an initial background IB. The length of the predetermined initial background extraction period is preferably set to, for example, 100 to 140 seconds. The predetermined initial background extraction period is preferably set to a relatively early period within the predetermined measurement period of the range sensor 2, and is preferably set so that, for example, the start time of the predetermined measurement period of the range sensor 2 and the start time of the predetermined initial background extraction period coincide with each other.

[0027] Fig. 6 shows a schematic diagram of the analysis data obtained after performing the initial background extraction step S1 (initial background extraction process) on the analysis data of Fig. 5. In Fig. 6, cell C registered as the initial background IB is shown colored gray.

[0028] The initial background extraction step S1 includes, for example, an object extraction step S11, a high-position stationary object extraction step S12, and a linear stationary object extraction step S13.

[0029] <Object extraction step (S11)> In the object extraction step S11, the processing unit 31 performs an object extraction process to extract one or more objects O based on the point cloud data output from the range measurement sensor 2 at the measurement time within a predetermined initial background extraction period. The processing unit 31 repeats the object extraction step S11 at each measurement time within a predetermined initial background extraction period. Specifically, as shown in FIG. 5, the processing unit 31 performs, for example, the following steps S111 to S112 in the object extraction step S11.

[0030] First, the processing unit 31 stores the point cloud data output from the range measurement sensor 2 at the latest measurement time within a predetermined initial background extraction period in a cell array CA (cell array storage step S111). Here, the processing unit 31 stores each point of the point cloud data in a corresponding cell C in the cell array CA based on the x-coordinate and y-coordinate of each point of the point cloud data. Next, as shown in FIG. 5, the processing unit 31 extracts a point cloud consisting of multiple adjacent points as one point cloud cluster and thus as an object O by clustering, and assigns an individual ID to each object O (clustering step S112). At this time, when assigning an ID to the object O extracted based on the point cloud data at the current measurement time, if each cell C constituting the object O at least partially overlaps with each cell C constituting any one object O extracted based on the point cloud data at the previous measurement time, the processing unit 31 inherits the ID assigned to the object O extracted based on the point cloud data at the previous measurement time. This makes it possible to track the object O between different measurement times. Note that "each cell C constituting the object O" refers to each cell C in which a point cloud constituting the object O exists. Any known method may be used as a specific clustering method. For reference, in Fig. 5, the ID assigned to each object O extracted in the object extraction step S11 is shown. In the following, the IDs assigned to the object O may be collectively referred to as "i", "k", etc.

[0031] <High-position stationary object extraction step (S12)> In the high-position stationary object extraction step S12, the processing unit 31 performs a high-position stationary object extraction process in which, from among the one or more objects O extracted in the object extraction step S11, one or more objects O (hereinafter referred to as "first objects O"), at least a portion of which is located at a height position equal to or higher than a predetermined height, are extracted as stationary objects SO, and each cell C constituting each stationary object SO in the cell array CA is registered as an initial background IB. The processing unit 31 may perform the high-position stationary object extraction step S12 only once using the result (one or more objects O) of the object extraction step S11 performed based on the point cloud data at the first measurement time of the specified initial background extraction period, or may perform the high-position stationary object extraction step S12 using the result of the object extraction step S11 each time the object extraction step S11 is performed. When the processing unit 31 extracts the first object O as a stationary object SO and registers it as an initial background IB in the high-position stationary object extraction step S12, it cancels the ID assigned to the first object O in the object extraction step S11 (FIG. 6). Here, the predetermined height is preferably set slightly higher than the person P in the predetermined space S, and is preferably set to a height of 1.75 to 1.90 m from the ground, and more preferably set to a height of 1.80 to 1.85 m from the ground. The predetermined height is preferably set to the same height as the installation height of the range sensor 2, which is set slightly higher than the person P in the predetermined space S. By defining an object O located at a relatively high position as a stationary object SO in the high-position stationary object extraction step S12, the number of points to be processed subsequently can be reduced accordingly, thereby improving the efficiency and real-time performance of the processing by the analysis device 3.

[0032] <Straight-line stationary object extraction step (S13)> In the linear stationary object extraction step S13, the processing unit 31 calculates a first linearity evaluation value C that indicates the degree of linearity of each of one or more objects O (hereinafter referred to as "second objects O") other than the one or more first objects O extracted in the high-position stationary object extraction step S12, from among the one or more objects O extracted in the object extraction step S11. cluster (i) is calculated, and the first linearity evaluation value C cluster (i) is the predetermined first linearity evaluation value threshold C th_cluster Then, one or more second objects O exceeding the number of pixels in the image are extracted as stationary objects SO, and each cell C constituting each stationary object SO in the cell array CA is registered as an initial background IB, thereby performing a linear stationary object extraction process. Generally speaking, a still object SO extracted by the linear still object extraction process can be said to have a surface shape with higher linearity (more linear components) than a person P. Note that, as a method of extracting a still object SO, it is possible to extract an object O that has not moved at all during a predetermined initial background extraction period as a still object SO, but in that case, there is a risk that a person P that has not moved at all during the predetermined initial background extraction period will also be extracted as a still object SO. Therefore, in this example, instead of focusing on the movement of the object O, attention is paid to the linearity of the surface shape of the object O, thereby avoiding such a risk and enabling a still object SO to be extracted more accurately.

[0033] Specifically, in the linear stationary object extraction step S13, the processing unit 31 performs, for each second object O, for example, the following steps S131 to S138.

[0034] First, the processing unit 31 stores the point cloud cluster of the second object O obtained as described above from the point cloud data at the measurement time immediately prior to the predetermined initial background extraction period in a virtual voxel array VA (not shown). Here, the processing unit 31 stores each point of the point cloud cluster of the second object O in the corresponding voxel V in the voxel array VA based on the x-coordinate, y-coordinate, and z-coordinate of each point of the point cloud cluster of the second object O (voxel array storage step S131). The voxel array VA is formed by arranging cubic voxels V (not shown) three-dimensionally along the x-axis, y-axis, and z-axis. The length of one side of the voxels V is the same as the length of one side of the cells C in the cell array CA. The coordinates of each voxel V are expressed as (pq, r) in the xyz Cartesian coordinate system. Here, p, q, and r are preferably integers.

[0035] Next, the processing unit 31 calculates the center point P on the xy plane of the point cloud stored in any one voxel V(p, q, r) of the voxels V that constitute the second object O. center =(P center:x ,P center:y ) is calculated (center point calculation step S132). center Specifically, "V" refers to the average point or the center of gravity point. Here, "each voxel V constituting the second object O" refers to each voxel V in which the point group constituting the second object O is stored. Next, the processing unit 31 calculates the center point P center and each point P stored in the voxel n =(P n:x ,P n:y ,P n:z ) and the angle θ in the xy plane direction n are calculated by the following equation (1) (angle calculation step S133). n is a pair of points P center , P n This is the angle between the x-axis and the line segment formed by projecting the line segment connecting the two points onto the xy plane.

number

[0036] Next, the processing unit 31 calculates the angle θ n Angle θ n The average value of θ ave and the angle standard deviation σ θ (p, q, r) is calculated by the following equation (2) (angle standard deviation calculation step S134).

number

[0037] Next, the processing unit 31 calculates the angle standard deviation σ θ Using (p, q, r), the background weight ω of the voxel V is calculated. BG (p, q, r) is calculated by the following equation (3) (background weight calculation step S135).

number

[0038] Similarly, the processing unit 31 performs steps S132 to S135 for all of the voxels V that make up the second object O, and calculates the background weight ω BG Find (p,q,r) respectively.

[0039] Next, the processing unit 31 calculates the background weights ω BG Average value of (p,q,r) ω BGave (p, q, r) is calculated (background weighted average value calculation step S136). Similarly, the processing unit 31 performs steps S131 to S136 at each measurement time within the predetermined initial background extraction period to calculate each background weight ω BG Average value of (p,q,r) ω BGave Calculate (p,q,r).

[0040] Next, the processing unit 31 calculates the background weights ω of the second object O for each point cloud data at each measurement time within the predetermined initial background extraction period. BGAverage value of (p,q,r) ω BGave By adding (p, q, r) together, the first linearity evaluation value C of the second object O is obtained. cluster (i) is calculated (first linearity evaluation value calculation step S137). First linearity evaluation value C cluster (i) represents the degree of linearity of the surface shape of the second object O, and the higher the value, the higher the linearity of the surface shape. cluster The i in (i) represents the ID of the second object O.

[0041] Next, the processing unit 31 calculates the first linearity evaluation value C cluster (i) and a predetermined first linearity evaluation value threshold C th_cluster and the first linearity evaluation value C cluster (i) is the predetermined first linearity evaluation value threshold C th_cluster If the number of second objects O exceeds the threshold value, the second objects O are extracted as stationary objects SO, and each cell C constituting each stationary object SO in the cell array CA is registered as an initial background IB (initial background registration step S138). Predetermined first linearity evaluation value threshold C th_cluster is preferably 960, for example.

[0042] When the processing unit 31 extracts the second object O as the stationary object SO and registers it as the initial background IB in the linear stationary object extraction step S13, it cancels the ID assigned to the second object O in the object extraction step S11 (FIG. 6).

[0043] The initial background extraction step S1 may include only one of the high-position stationary object extraction step S12 and the linear stationary object extraction step S13. Furthermore, in the initial background extraction step S1, the processing unit 31 may extract the object O as the stationary object SO by a method different from the high position stationary object extraction step S12 and the linear stationary object extraction step S13 described above.

[0044] -Animal body extraction step (S2)- In the moving object extraction step S2, the processing unit 31 performs a moving object extraction process to extract one or more moving objects MO by subtracting the initial background IB registered in the initial background extraction step S1 from the point cloud data output from the range sensor 2 at the measurement time within a specified observation period after the specified initial background extraction period. The specified observation period may be set to any period after the specified initial background extraction period within the specified measurement period of the range sensor 2, and is preferably set to the entire period after the specified initial background extraction period within the specified measurement period of the range sensor 2, for example. The processing unit 31 repeats the moving object extraction step S2 at each measurement time within a predetermined observation period. FIG. 7 shows a schematic diagram of point cloud data in the predetermined space of FIG. 4 at a certain measurement time within a predetermined observation period. Specifically, the processing unit 31 performs the following steps S21 to S23 in the moving object extraction step S2.

[0045] 7, the processing unit 31 stores the point cloud data output from the range measurement sensor 2 at the latest measurement time within a predetermined observation period in a cell array CA (cell array storage step S21). Here, the processing unit 31 stores each point of the point cloud data in a corresponding cell C in the cell array CA based on the x-coordinate and y-coordinate of each point of the point cloud data. Next, the processing unit 31 subtracts the initial background IB registered in the initial background extraction step S1 from the point cloud data, thereby leaving only the point cloud data stored in cells C other than the cell C of the initial background IB as the target for subsequent processing (subtraction step S22). All of this remaining point cloud data becomes point cloud data of the moving object MO. Next, as shown in Fig. 7, the processing unit 31 performs clustering on the point cloud data remaining to be processed, extracting a point cloud consisting of multiple adjacent points from the point cloud data as a single point cloud cluster and therefore as a moving object MO, and assigns an individual ID to each moving object MO (clustering step S23). At this time, when assigning an ID to a moving object MO extracted based on the point cloud data at the current measurement time, if each cell C constituting the moving object MO at least partially overlaps with each cell C constituting any one moving object MO extracted based on the point cloud data at the previous measurement time, the processing unit 31 inherits the ID assigned to the one moving object MO extracted based on the point cloud data at the previous measurement time. This makes it possible to track moving objects MO between different measurement times. Any known method may be used as a specific clustering method. For reference, in Fig. 7, the ID assigned to each moving object MO extracted in the moving object extraction step S2 is shown.

[0046] - Movable and stationary object extraction step (S3) - In the movable stationary object extraction step S3, the processing unit 31 performs a movable stationary object extraction process in which one or more movable stationary objects MSO are extracted from one or more moving objects MO extracted in the moving object extraction step S2, and each cell C constituting each movable stationary object MSO in the cell array CA is redefined as a post-defined background LB. A moving object MO that has been redefined as a post-defined background LB is thereafter treated as a post-defined background LB and is no longer treated as a moving object MO. The processing unit 31 performs the movable stationary object extraction step S3 every time it performs the moving object extraction step S2 (that is, at each measurement time within a predetermined observation period).

[0047] Fig. 13 shows a schematic diagram of the analysis data obtained after performing the movable stationary object extraction step S3 (movable stationary object extraction process) on the analysis data of Fig. 7. In Fig. 13, the cell C redefined as the post-defined background LB is shown colored gray. 13, after the moving and stationary object extraction step S3, all moving objects MO made up of point cloud data stored in cells C of the cell array CA other than cells C of the initial background IB and the post-defined background LB are determined to be people P. In this way, the analysis system 1 becomes able to distinguish objects O in the predetermined space S into people P and objects other than people.

[0048] When the processing unit 31 extracts the moving object MO as a moving stationary object MSO in the moving stationary object extraction step S3 and redefines it as a post-defined background LB, it cancels the ID assigned to the moving object MO in the moving object extraction step S2 (FIG. 13).

[0049] The movable stationary object extraction step S3 includes, for example, an interlocking type movable stationary object extraction step S31, a linear type movable stationary object extraction step S32, and a fine-motion type movable stationary object extraction step S34.

[0050] <Interlocking Movable and Stationary Object Extraction Step (S31)> Generally, chairs are the most common type of movable stationary object MSO that exists in an indoor space. Chairs tend to move in conjunction with a series of actions, such as when a person sits down in a chair, stands up, and then leaves the chair. Therefore, in the linked movable stationary object extraction step S31, such actions of chairs and people are detected, and chairs are extracted as movable stationary objects MSO. In the linked movable stationary object extraction step S31, the processing unit 31 determines whether one or more moving objects MO (hereinafter referred to as "first moving objects MO") among the one or more moving objects MO extracted in the moving object extraction step S2 transition from a sitting state to a standing state, and then a new moving object MO is extracted around the first moving object MO by the moving object extraction step S2 (moving object extraction processing), and then, the time T during which the following formula (5) is continuously satisfied within a predetermined new moving object monitoring time from the time when the formula (5) is first satisfied: chairIf the time (seconds) exceeds a predetermined duration threshold, the new moving object MO is extracted as a movable stationary object MSO, and each cell C that constitutes the movable stationary object MSO in the cell array CA is redefined as a post-defined background LB, thereby performing an interlocking movable stationary object extraction process.

number

[0051] Specifically, in the linked movable stationary object extraction step S31, the processing unit 31 performs, for example, the following steps S311 to S316.

[0052] First, the processing unit 31 determines whether each moving object MO is in a sitting state (sitting state determination step S311). Here, the sitting state is assumed to be a state in which a person is sitting on a chair. On the other hand, the standing state is assumed to be a state in which a person is standing. The processing unit 31 performs the sitting position determination step S311 at each measurement time within a predetermined observation period. Here, a specific method for the position determination step S311 will be described. Each cell C constituting the moving object MO stores the z coordinate value of the highest point among the group of points contained in that cell C. The height h(i,t) (mm) of the moving object MO (ID is assumed to be "i") at a certain measurement time t is defined as the z coordinate value of the highest point among the cells C constituting the moving object MO at that measurement time t. In addition, the maximum height h of the moving object MO is max (i) (mm) is the maximum value of the height h(i,t) of the moving object MO acquired up to the measurement time t. Also, the threshold value h sit (i) (mm) is defined by the following formula (4).

number

[0053] When the processing unit 31 determines in the sitting position determination step S311 that one or more moving objects MO (hereinafter referred to as "first moving objects MO") among the moving objects MO are in a sitting position, the processing unit 31 stores the height h(i, t) of the first moving object MO at that time in each cell C within a radius of 50 cm from the center P(i) of the first moving object MO (ID: i), and further stores the average value h(i, t) of the heights h(i, t) stored in each cell C up to that point. sit_ave (p, q) (hereinafter referred to as "average sitting height h sit_ave (p, q)" is calculated (average sitting position height calculation step S312). Here, "the center P(i) of the first moving object MO (with ID "i")" means the average point or center of gravity of the group of points that make up the first moving object MO in the xy coordinate system. The processing unit 31 performs the sitting position height average value calculation step S312 for each moving object MO every time it is determined that the moving object MO is in a sitting position in the sitting position determination step S311.

[0054] Furthermore, if the processing unit 31 determines in the sitting position determination step S311 that the first moving object MO is in a sitting position, it continues to monitor the first moving object MO by tracking, and then determines whether the first moving object MO transitions to a standing position (i.e., the first moving object MO is determined to be in a standing position in the subsequent sitting position determination step S311), and then determines whether a new moving object MO (with ID ``k'') is extracted around the first moving object MO in the moving object extraction step S2 (monitoring step S313). Here, "the vicinity of the first moving object MO" specifically refers to, for example, an area within a radius of 50 cm from the center of each cell C that is within a radius of 50 cm from the center P(i) of the first moving object MO. In addition, when a new moving object MO (ID: k) is extracted around the first moving object MO in moving object extraction step S2, it is assumed that a transition has occurred from a state in which the chair and the person sitting on or standing next to it were detected as a single moving object MO (i.e., a state in which only one ID was assigned to the chair and the person; see Figure 6), to a state in which the chair and the person have been detected as separate moving objects MO because the person has left the chair (i.e., a state in which the ID is increased by one and separate IDs are assigned to the chair and the person; see Figure 7).

[0055] If the processing unit 31 determines in the monitoring step S313 that the first moving object MO (ID: i) has transitioned from a sitting state to a standing state, and then determines that a new moving object MO (ID: k) has been extracted around the first moving object MO in the moving object extraction step S2, it determines whether the following equation (5) is satisfied (height determination step S314).

number

[0056] If it is determined in the height determination step S314 that the formula (5) is satisfied, the processing unit 31 determines the time T during which the formula (5) is continuously satisfied, as a candidate for the new moving object MO as a movable stationary object. chair (seconds) is measured (time measurement step S315). In the time measurement step S315, the processing unit 31 calculates the time T chair If the time exceeds a predetermined duration threshold, the new moving object MO is extracted as a movable stationary object MSO, and each cell C constituting the movable stationary object MSO in the cell array CA is redefined as a post-defined background LB (redefinition step S316). The predetermined new moving object monitoring time is preferably, for example, 100 to 140 seconds, and more preferably, 120 seconds, and the predetermined duration threshold is preferably, for example, 5 to 20 seconds, and more preferably, 12 seconds.

[0057] When the processing unit 31 extracts the new moving object MO as a movable stationary object MSO and redefines it as a post-defined background LB in the redefinition step S316, it cancels the ID assigned to the new moving object MO in the moving object extraction step S2 (Figure 13).

[0058] <Straight-line moving and stationary object extraction step (S32)> Generally, among the movable stationary objects MSO that exist in an indoor space, the second most common after chairs are whiteboards, desks, etc. Most of these have flat surfaces, and are represented as a collection of linear points in the point cloud data obtained from the range sensor 2. Therefore, in the linear movable stationary object extraction step S32, the linear shape of the surface of the moving object MO is extracted, a second linearity evaluation value is calculated based on the extracted linearity evaluation value, and the whiteboard, desk, etc. are extracted as movable stationary objects MSO based on the second linearity evaluation value. In the linear movable stationary object extraction step S32, when an occluded area SR appears in the initial background IB at the same location over a predetermined occluded area monitoring time, the processing unit 31 identifies a moving object MO (hereinafter referred to as a "second moving object MO") located between the range measurement sensor and the occluded area SR from among one or more moving objects MO extracted in the moving object extraction step S2, and calculates a second linearity evaluation value C BG (t, i) is calculated, and the second linearity evaluation value C BG (t, i) is the predetermined second linearity evaluation value threshold C th_BG If the difference exceeds the threshold, the second moving object MO is extracted as a movable stationary object MSO, and each cell C constituting the movable stationary object MSO in the cell array CA is redefined as a post-defined background LB, thereby performing a linear movable stationary object extraction process.

[0059] Specifically, in the linear movable stationary object extraction step S32, the processing unit 31 performs, for example, the following steps S321 to S331.

[0060] First, the processing unit 31 determines whether or not a masked region SR has appeared in the initial background IB at the same location over a predetermined masked region monitoring time based on the point cloud data stored in the cell array CA (masked region monitoring step S321). If the movable stationary object MSO is moved by a person during the predetermined observation period, an occluded region SR (shadow) is generated in the initial background IB in the cell array CA by the movable stationary object MSO. The occluded region SR is an area that is not detected by the range sensor 2 due to the movable stationary object MSO being between the range sensor 2 and the occluded region SR, and is an area in the cell array CA where no point cloud exists. For example, in the example of FIG. 7, a whiteboard serving as the movable stationary object MSO (ID: 8) is moved into the predetermined space S during the predetermined observation period, and as a result, an occluded region SR is generated in the initial background IB by the whiteboard. Specifically, in the occlusion area monitoring step S321, if at least one cell C among the cells C constituting the initial background IB does not contain a point cloud over a predetermined occlusion area monitoring time, the processing unit 31 determines that an occlusion area SR consisting of the at least one cell C has appeared in the initial background IB at the same location over the predetermined occlusion area monitoring time.

[0061] If the processing unit 31 determines in the shielded area monitoring step S321 that a shielded area SR has appeared in the initial background IB at the same location for a predetermined shielded area monitoring time, it identifies, among the moving objects MO, the moving object MO (hereinafter referred to as the "second moving object MO") that is located between the range sensor 2 and the shielded area SR (second moving object identification step S322). It should be noted that there is a possibility that the second moving object MO is the movable stationary object MSO that caused the occlusion region SR. Therefore, the second moving object MO is set as a candidate for the movable stationary object MSO, and the following processing is performed on the second moving object MO. When the processing unit 31 has identified a plurality of second moving objects MO for each of the plurality of shield regions SR in steps S321 to S322, the processing unit 31 performs the following process for each of the second moving objects MO.

[0062] Next, as shown in FIG. 9, the processing unit 31 calculates the center points P of the cells C(p, q) constituting the second moving object MO. cell (cell center point calculation step S323). Here, the center point P of the cell C(p,q) is calculated. cell means the average point or center of gravity of the point group stored in the cell C. The cell center point calculation step S323 can reduce the number of point groups to be processed thereafter, improving the efficiency and real-time nature of the processing.

[0063] Next, the processing unit 31 calculates a plurality of center points P constituting the second moving object MO. cell the center point P cell Then, the pair of adjacent center points P cell The Euclidean distance d between n (Fig. 9) is a pair of adjacent center points P cell (Euclidean distance calculation step S324). In the Euclidean distance calculation step S324, the processing unit 31 calculates, for example, the distance between the plurality of center points P constituting the second moving object MO. cell , P cell Then, sort the x-coordinates of the points in ascending order and select the center point P cell (The smallest x-coordinate is P cell ) in order, the Euclidean distance d n (The center point P cell and the center point P cell The other center point P closest to cell The Euclidean distance d between n ) is preferable. In this case, one center point P cell For (center point P cell (each) one Euclidean distance d n If there are multiple center points P at the same x-coordinate, cell If there are multiple center points P cell the center point P cell Then, a pair of center points P adjacent to each other in the y-axis direction are sorted in ascending order of y coordinates. cell The Euclidean distance d between n A pair of adjacent center points Pcell Here, n (n=2, 3...N) indicates the number of cells C that make up the second moving object MO.

[0064] Next, the processing unit 31 calculates the Euclidean distance d n The average value of d n_ave Calculate the average value d as shown in Figure 10. n_ave By this, multiple center points P constituting the second moving object MO cell is divided into two divided clusters (cluster division step S325). At this time, one of the divided clusters (hereinafter referred to as the "first divided cluster") is divided into two divided clusters, each of which has a plurality of center points P cell Among them, the average value d n_ave Euclidean distance less than d n The center point P corresponding to cell The other divided cluster (hereinafter referred to as the "second divided cluster") is composed of a plurality of center points P cell Among them, the average value d n_ave Euclidean distance d or greater n The center point P corresponding to cell The system should consist of the following: The inventor of the present invention has found through various analyses that the Euclidean distance d n It has been found that the distribution of has two maximum values ​​(peaks), as shown in FIG. 10. The cluster division step S325 is based on this finding. Here, the "Euclidean distance d n As shown in Figure 10, the horizontal axis is the Euclidean distance d n The vertical axis is d n d with each value for the total number of n This means a graph showing the ratio of the number of

[0065] Next, the processing unit 31 calculates a plurality of center points P constituting each of the first and second divided clusters. cell A pair of adjacent center points P cell=(P cell,x:k ,P cell,y:k ), P cell =(P cell,x:k+1 ,P cell,y:k+1 ) angle θ m (Fig. 11) is calculated by the following equation (6) to find a pair of adjacent center points P cell (angle calculation step S326). m is a pair of adjacent center points P cell The angle between the two points and the x-axis is the angle between the two points and the x-axis.

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[0066] Next, the processing unit 31 calculates the angle θ calculated in the angle calculation step S326 for each of the first divided cluster and the second divided cluster. m Using this, the straightness μ (degrees) is calculated by the following equation (7) (straightness calculation step S327).

number

[0067] Next, the processing unit 31 calculates a shape score ε for each of the first divided cluster and the second divided cluster using the straightness μ calculated in the straightness calculation step S327 according to the following equation (8) (shape score calculation step S328).

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[0068] Next, the processing unit 31 calculates the larger shape score ε out of the shape scores ε calculated for the first divided cluster and the second divided cluster in the shape score calculation step S328 as a large shape score ε. max The smaller shape score ε is defined as the small shape score ε min (size and small shape score determination step S329).

[0069] Next, the processing unit 31 calculates the second linearity evaluation value C at the most recent measurement time t. BG (t, i) is calculated by the following equation (9) (second linearity evaluation value calculation step S330).

number

[0070] The processing unit 31 calculates the second linearity evaluation value C within a predetermined second linearity evaluation time from the time when the second moving object MO is identified in the second moving object identification step S322. BG (t, i) is the predetermined second linearity evaluation value threshold C th_BG If it exceeds this limit, the second moving object MO is extracted as a movable stationary object MSO, and each cell C constituting the movable stationary object MSO in the cell array CA is redefined as a post-defined background LB (redefinition step S331). The predetermined second linearity evaluation time is preferably, for example, 100 to 140 seconds, and is preferably, for example, 120 seconds. th_BG For example, 960 is preferable.

[0071] When the processing unit 31 extracts the second moving object MO as a movable stationary object MSO and redefines it as a post-defined background LB in the redefinition step S331, it cancels the ID assigned to the second moving object MO in the moving object extraction step S2 (Figure 13).

[0072] <Slightly moving stationary object extraction step (S34)> Generally, as movable stationary objects MSO indoors, in addition to the movable stationary objects MSO extracted in the above-mentioned linked movable stationary object extraction step S31 and linear movable stationary object extraction step S32, there are also movable stationary objects MSO whose shapes vary depending on the environment and whose timing and conditions of movement vary, such as houseplants, trash cans, and laptops. The biggest difference between these movable stationary objects MSO and people is whether or not they actively move. While people can actively move, these movable stationary objects MSO do not move actively, so the amount of movement is slight. Therefore, in the slight-movement movable stationary object extraction step S34, moving objects MO that move only slightly are extracted as movable stationary objects MSO.

[0073] The slight movement type movable stationary object extraction step S34 includes, for example, a highest point slight fluctuation type movable stationary object extraction step S341 and a voxel number slight fluctuation type movable stationary object extraction step S342.

[0074] <Step for extracting moving and stationary objects with slight fluctuations in the highest point (S341)> In the highest point slight fluctuation type movable stationary object extraction step S341, the amount of movement of the highest point of the moving object MO (and consequently the position distribution of the highest point) is focused on, and the moving object MO with slight movement is extracted as the movable stationary object MSO. In the highest point slight variation type movable stationary object extraction step S341, the processing unit 31 performs a highest point slight variation type movable stationary object extraction process in which, if the highest point of one or more moving objects MO (hereinafter referred to as "third moving objects MO") among one or more moving objects MO extracted in the moving object extraction step S2 is located at approximately the same position over a predetermined highest point monitoring time, the processing unit 31 extracts the third moving object MO as a movable stationary object MSO and redefines each cell C that constitutes the movable stationary object MSO in the cell array CA as a post-defined background LB.

[0075] As described above in the description of the position determination step S311, each cell C constituting the moving object MO stores the z coordinate value of the highest point (highest point) among the group of points contained in that cell C. The height h(i, t) (mm) of the moving object MO (with ID "i") at the most recent measurement time t is defined as the z coordinate value of the highest point (highest point) among the cells C constituting the moving object MO at that measurement time t. In the highest point slight fluctuation type movable stationary object extraction step S341, the processing unit 31 performs, for example, the following steps S3411 to S3412.

[0076] First, for each moving body MO, the processing unit 31 stores the position (p, q) of one or more cells C having the height h(i, t) (highest point) of the moving body MO for a predetermined highest point cell storage time (highest point cell storage step S3411). Here, the predetermined highest point cell storage time is preferably, for example, 100 to 140 seconds, and is preferably, for example, 120 seconds.

[0077] Next, if one or more of the one or more moving bodies MO (hereinafter referred to as "third moving bodies MO") among the one or more moving bodies MO have one or more cells C that have been constantly saved for a specified highest point cell saving time in the highest point cell saving step S3411, the processing unit 31 determines that the highest points of these third moving bodies MO have been located at approximately the same position for the specified highest point monitoring time, extracts each third moving body MO as a movable stationary object MSO, and redefines each cell C that constitutes the movable stationary object MSO in the cell array CA as a post-defined background LB (redefinition step S3412).

[0078] When the processing unit 31 extracts the third moving object MO as a movable stationary object MSO and redefines it as a post-defined background LB in the redefinition step S3412, it cancels the ID assigned to the third moving object MO in the moving object extraction step S2 (Figure 13).

[0079] <Step for extracting moving and stationary objects with slight variations in the number of voxels (S342)> The position distribution of the highest point of the moving object MO focused on in the highest point slight variation type moving stationary object extraction step S341 may vary even for moving stationary objects MSO that do not actively move, when the highest point position exists on the boundary between cells C or due to the influence of errors in the range sensor 2. To enable the moving stationary object MSO to be extracted even in such cases, the voxel number slight variation type moving stationary object extraction step S342 focuses on the variation in the number of voxels that make up the moving object of the moving object MO, and extracts moving objects MO with slight movement as moving stationary objects MSO. In the voxel number slight variation type movable stationary object extraction step S342, the processing unit 31 determines whether one or more moving objects MO (hereinafter referred to as "fourth moving object MO") among the one or more moving objects MO extracted in the moving object extraction step S2 is within a predetermined voxel number monitoring time t thIf a voxel number fluctuation evaluation value representing the degree of fluctuation in the number of voxels V(i,t) constituting the moving body of the fourth moving body MO within the moving body MO is less than a predetermined voxel number fluctuation evaluation value threshold, the fourth moving body MO is extracted as a movable stationary object MSO, and each cell C constituting the movable stationary object MSO in the cell array CA is redefined as a post-defined background LB, thereby performing a voxel number slight fluctuation type movable stationary object extraction process.

[0080] As described above in the description of the locus determination step S311, each cell C constituting the moving object MO stores the z coordinate value of the highest point (highest point) among the group of points contained in that cell C. In the voxel number slight variation type movable stationary object extraction step S342, the processing unit 31 performs, for example, the following steps S3421 to S3424.

[0081] First, the processing unit 31 calculates the number of voxels V(i, t) constituting the moving object MO at the most recent measurement time t by the following equations (10) and (11) for a predetermined voxel number monitoring time t. th The calculation is performed at predetermined measurement time intervals (that is, at each measurement time t) over the entire moving body (moving body constituting voxels calculation step S3421). The number of voxels constituting the moving body V(i, t) represents the number of voxels V estimated to constitute the moving body MO in the voxel array VA. Here, the monitoring time for a given number of voxels is t th is preferably, for example, 100 to 140 seconds, and more preferably, 120 seconds.

number

number

[0082] Next, the processing unit 31 calculates the voxel number change amount V by the following equation (12): dif (i, t) is calculated (voxel number change calculation step S3422).

number

[0083] Next, the processing unit 31 calculates the predetermined number of voxels monitoring time t th Change in number of voxels per minute V dif The voxel number fluctuation evaluation value σ, which is the standard deviation of (i, t) dif (i) is calculated (voxel number fluctuation evaluation value calculation step S3423).

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[0084] Next, the processing unit 31 calculates the voxel number fluctuation evaluation value σ calculated in the voxel number fluctuation evaluation value calculation step S3423. dif Based on (i), a determination is made as to whether the fourth moving object MO is a movable stationary object MSO, and if it is determined that the fourth moving object MO is a movable stationary object MSO, the fourth moving object MO is extracted as a movable stationary object MSO, and each cell C constituting the movable stationary object MSO in the cell array CA is redefined as a post-defined background LB (redefinition step S3424). In the redefinition step S3424, the processing unit 31 redefines σ dif(i) If <5.0, the fourth moving object MO is determined to be a movable stationary object MSO, where 5.0 in this example corresponds to the above-mentioned predetermined voxel number fluctuation evaluation value threshold. In the redefinition step S3424, the processing unit 31 determines whether 5.0≦σ dif (i) If <6.0, the predetermined number of voxels is monitored for a time t th Half of (t th / 2), and the voxel number fluctuation evaluation value σ dif (i) is σ dif (i) Only if <5.0 is satisfied, the fourth moving object MO is determined to be a movable stationary object MSO. In the redefinition step S3424, the processing unit 31 determines whether 6.0≦σ dif If the condition is (i), it is determined that the fourth moving object MO is not a movable stationary object MSO (that is, it is a person P). The inventor of the present invention has found through various analyses that σ dif (i) If < 5.0, the moving object MO is a moving stationary object MSO, and 6.0 ≤ σ dif If (i), then the animal object MO is a person P, and 5.0≦σ dif (i) When the value is less than 6.0, the moving object MO is highly likely to be a person P, but there is also a possibility that the moving object MO is a movable stationary object MSO. The redefinition step S3424 described above was set based on this knowledge.

[0085] When the processing unit 31 extracts the fourth moving object MO as a movable stationary object MSO and redefines it as a post-defined background LB in the redefinition step S3424, it cancels the ID assigned to the fourth moving object MO in the moving object extraction step S2 (Figure 13).

[0086] 13, after the moving and stationary object extraction step S3, all moving objects MO consisting of point cloud data stored in cells C of the cell array CA other than cells C of the initial background IB and the post-defined background LB are determined to be people P. After the moving and stationary object extraction step S3, the ID is assigned only to people P. In this way, the analysis system 1 becomes able to distinguish objects O in the predetermined space S into people P and objects other than people.

[0087] From the viewpoint of improving processing efficiency and real-time performance, it is preferable to first perform the linked movable stationary object extraction step S31, then the linear movable stationary object extraction step S32, and then the fine-motion movable stationary object extraction step S34 in the movable stationary object extraction step S3. However, the linked movable stationary object extraction step S31, the linear movable stationary object extraction step S32, and the fine-motion movable stationary object extraction step S34 may be performed in any order, or two or three of them may be performed simultaneously in parallel. Furthermore, from the viewpoint of improving processing efficiency and real-time performance, it is preferable that the fine-movement type movable stationary object extraction step S34 first performs the highest point fine-movement type movable stationary object extraction step S341, and then performs the voxel number fine-movement type movable stationary object extraction step S342. However, the highest point slight variation type movable stationary object extraction step S341 and the voxel number slight variation type movable stationary object extraction step S342 may be performed in the reverse order, or may be performed simultaneously in parallel.

[0088] In addition, the movable stationary object extraction step S3 may include any one or two of the above-mentioned linked movable stationary object extraction step S31, linear movable stationary object extraction step S32, and fine-motion movable stationary object extraction step S34. Furthermore, the slight movement type movable stationary object extraction step S34 may include only one of the above-mentioned highest point slight fluctuation type movable stationary object extraction step S341 and the voxel number slight fluctuation type movable stationary object extraction step S342. Furthermore, in the movable stationary object extraction step S3, the processing unit 31 may extract the moving object MO as the movable stationary object MSO by a method different from the above-described method.

[0089] -Environmental map creation step (S4)- In the environmental map creation step S4, the processing unit 31 creates an environmental map K based on the analysis results of the point cloud data for each measurement time within the predetermined observation period obtained in the movable / stationary object extraction step S3. The environmental map creation step S4 may be performed after the movable stationary object extraction step S3, or may be performed in parallel with the movable stationary object extraction step S3. The environmental map K preferably represents how a predetermined space S is used by people. The above-described steps S1 to S3 make it easy to grasp the number and positions of people P at each measurement time within a predetermined observation period, as well as the positions and shapes of objects other than people P registered as the initial background IB and the post-defined background LB, making it possible to more accurately represent how the predetermined space S is used by people. The processing unit 31 recognizes an object O to which an ID has been assigned as a person P. FIG. 14 shows an example of the environmental map K. As shown in Fig. 14, it is preferable that the environmental map K is represented using a cell array CA. In addition, it is preferable that in the cell array CA, the cells C of the initial background IB and the later-defined background LB are displayed using a different notation (such as a color or hatching) from the other cells C. For example, the environmental map K may display, in each cell C other than the initial background IB and the later-defined background LB, how much the space of the cell C was utilized by the person P within a predetermined observation period (degree of activity) using any notation method (for example, color intensity or type of hatching, etc.) as in the example of Fig. 14. In the example of Fig. 14, the initial background IB and the later-defined background LB are displayed in black, and in each cell C other than the initial background IB and the later-defined background LB, the higher the degree of activity of the cell C, the darker the gray color displayed. Additionally, for example, the environmental map K may display, in each cell C other than the initial background IB and the post-defined background LB, the direction of movement of person P, the speed of person P, or the acceleration of person P in the space of that cell C within a specified observation period using any notation method (for example, color intensity or type of hatching, etc.). As a specific method for the environmental map creation step S4, for example, the method described in Japanese Patent Application No. 2020-038043 may be used.

[0090] As explained above, according to the above-mentioned embodiment, the processing unit 31 performs an initial background extraction step S1 (initial background extraction processing) for extracting stationary objects SO, a moving object extraction step S2 (moving object extraction processing) for extracting moving objects MO, and a movable stationary object extraction step S3 (movable stationary object extraction processing) for extracting movable stationary objects MSO, thereby making it possible to distinguish objects O in the space S into people P and objects other than people (stationary objects SO and movable stationary objects MSO).

[0091] Furthermore, according to the above-described embodiment, the processing is efficient, so that the processing of the initial background extraction step S1 and the processing from the moving object extraction step S2 to the movable stationary object extraction step S3 can be performed within a predetermined measurement time interval (e.g., 0.1 seconds), enabling real-time processing. Therefore, this analysis system and analysis method can be suitably used for mobile robot navigation, etc. Furthermore, according to the above-described embodiment, in the initial background extraction step S1 and the movable / stationary object extraction step S3, objects are extracted by focusing on characteristics common to multiple types of objects. This eliminates the need to perform machine learning in advance for each object other than a person, which may have different shapes, movements, etc., and allows this analysis system and analysis method to be used in a variety of environments. [Industrial Applicability]

[0092] The analysis system and analysis method of the present invention are suitable for use in analyzing indoor spaces such as office spaces and school spaces. [Explanation of symbols]

[0093] 1. Analysis system 2. Range Sensor 3 Analysis device 31 Processing section 32 Communications Department 33 Storage section 34 Input section 35 Display section CA cell array C Cell VA Voxel Array V Voxel O, Oa~Og objects SO stationary object MO Animal MSO Movable Stationary Object P person S Predetermined space IB initial background Defined background after LB SR shielding area K Environmental Map

Claims

1. a range sensor that measures a distance to an object in a predetermined space at each measurement time for each predetermined measurement time interval and outputs three-dimensional point cloud data; a processing unit; An analysis system comprising: The processing unit an initial background extraction process for extracting one or more stationary objects based on the point cloud data output from the range measurement sensor at each of the measurement times within a predetermined initial background extraction period, and registering, as an initial background, each cell constituting each of the stationary objects in a cell array defined by overlooking the predetermined space and subdividing it into a grid; a moving object extraction process for extracting one or more moving objects by subtracting the initial background from the point cloud data output from the range measurement sensor at the measurement time within a predetermined observation period after the predetermined initial background extraction period; a movable stationary object extraction process for extracting one or more movable stationary objects from the one or more moving objects and redefining each cell constituting each of the movable stationary objects in the cell array as a post-defined background; and the processing unit recognizes, as a person, any of the moving objects formed by point cloud data stored in cells other than the cells of the initial background and the post-defined background in the cell array; The movable stationary object refers to an object that moves in response to interaction with a person, The processing unit, in the movable stationary object extraction process, a linked movable stationary object extraction process in which a first moving object among the one or more moving objects transitions from a sitting state to a standing state, and then a new moving object is extracted around the first moving object by the moving object extraction process, and then, if the time during which the following formula (5) is continuously satisfied exceeds a predetermined duration threshold within a predetermined new moving object monitoring time from the time when the formula (5) is first satisfied, the new moving object is extracted as a movable stationary object, and each cell constituting the movable stationary object in the cell array is redefined as a post-defined background; an analysis system configured to: [Equation 1] In the above formula (5), h(k,t): Height of the new moving object (mm) γ: A given real number between 0 and 200 h sit_ave (p, q): average sitting height of the first moving object (mm)

2. A range finder that measures the distance to an object in a predetermined space at each measurement time for each predetermined measurement time interval and outputs three-dimensional point cloud data; a processing unit; An analysis system comprising: The processing unit an initial background extraction process for extracting one or more stationary objects based on the point cloud data output from the range measurement sensor at each of the measurement times within a predetermined initial background extraction period, and registering, as an initial background, each cell constituting each of the stationary objects in a cell array defined by overlooking the predetermined space and subdividing it into a grid; a moving object extraction process for extracting one or more moving objects by subtracting the initial background from the point cloud data output from the range measurement sensor at the measurement time within a predetermined observation period after the predetermined initial background extraction period; a movable stationary object extraction process for extracting one or more movable stationary objects from the one or more moving objects and redefining each cell constituting each of the movable stationary objects in the cell array as a post-defined background; and the processing unit recognizes, as a person, any of the moving objects formed by point cloud data stored in cells other than the cells of the initial background and the post-defined background in the cell array; The movable stationary object refers to an object that moves in response to interaction with a person, The processing unit, in the movable stationary object extraction process, a linear-type movable stationary object extraction process in which, when an obstructed area appears in the initial background at the same location over a predetermined obstructed area monitoring time, a second moving object located between the range measurement sensor and the obstructed area is identified from among the one or more moving objects, a second linearity evaluation value representing the degree of linearity of the object is calculated for the second moving object, and when the second linearity evaluation value exceeds a predetermined second linearity evaluation value threshold, the second moving object is extracted as a movable stationary object, and each cell constituting the movable stationary object in the cell array is redefined as a later-defined background; an analysis system configured to:

3. A range finder that measures the distance to an object in a predetermined space at each measurement time for each predetermined measurement time interval and outputs three-dimensional point cloud data; a processing unit; An analysis system comprising: The processing unit an initial background extraction process for extracting one or more stationary objects based on the point cloud data output from the range measurement sensor at each of the measurement times within a predetermined initial background extraction period, and registering, as an initial background, each cell constituting each of the stationary objects in a cell array defined by overlooking the predetermined space and subdividing it into a grid; a moving object extraction process for extracting one or more moving objects by subtracting the initial background from the point cloud data output from the range measurement sensor at the measurement time within a predetermined observation period after the predetermined initial background extraction period; a movable stationary object extraction process for extracting one or more movable stationary objects from the one or more moving objects and redefining each cell constituting each of the movable stationary objects in the cell array as a post-defined background; and the processing unit recognizes, as a person, any of the moving objects formed by point cloud data stored in cells other than the cells of the initial background and the post-defined background in the cell array; The movable stationary object refers to an object that moves in response to interaction with a person, The processing unit, in the movable stationary object extraction process, When the highest point of a third moving object among the one or more moving objects is located at approximately the same position over a predetermined highest point monitoring time, the third moving object is extracted as a movable stationary object, and each cell constituting the movable stationary object in the cell array is redefined as a post-defined background. an analysis system configured to:

4. A range finder that measures the distance to an object in a predetermined space at each measurement time for each predetermined measurement time interval and outputs three-dimensional point cloud data; a processing unit; An analysis system comprising: The processing unit an initial background extraction process for extracting one or more stationary objects based on the point cloud data output from the range measurement sensor at each of the measurement times within a predetermined initial background extraction period, and registering, as an initial background, each cell constituting each of the stationary objects in a cell array defined by overlooking the predetermined space and subdividing it into a grid; a moving object extraction process for extracting one or more moving objects by subtracting the initial background from the point cloud data output from the range measurement sensor at the measurement time within a predetermined observation period after the predetermined initial background extraction period; a movable stationary object extraction process for extracting one or more movable stationary objects from the one or more moving objects and redefining each cell constituting each of the movable stationary objects in the cell array as a post-defined background; and the processing unit recognizes, as a person, any of the moving objects formed by point cloud data stored in cells other than the cells of the initial background and the post-defined background in the cell array; The movable stationary object refers to an object that moves in response to interaction with a person, The processing unit, in the movable stationary object extraction process, a voxel number slight variation type movable stationary object extraction process, in which, when a voxel number variation evaluation value representing the degree of variation in the number of voxels constituting the fourth moving object of the one or more moving objects is less than a predetermined voxel number variation evaluation value threshold, the fourth moving object is extracted as a movable stationary object, and each cell constituting the movable stationary object in the cell array is redefined as a post-defined background; an analysis system configured to:

5. The processing unit, in the initial background extraction process, an object extraction process for extracting one or more objects based on the point cloud data output from the range measurement sensor at the measurement time within the predetermined initial background extraction period; a high-position stationary object extraction process for extracting, as stationary objects, one or more first objects at least a portion of which is located at a height position equal to or higher than a predetermined height from among the one or more objects extracted by the object extraction process, and registering, in the cell array, each cell constituting each of the stationary objects as the initial background; a linear stationary object extraction process that calculates a first linearity evaluation value representing the degree of linearity of one or more second objects other than the one or more first objects extracted by the high-position stationary object extraction process among the one or more objects extracted by the object extraction process, extracts one or more second objects whose first linearity evaluation value exceeds a predetermined first linearity evaluation value threshold as a stationary object, and registers each cell constituting each of the stationary objects in the cell array as the initial background; The analysis system according to any one of claims 1 to 4, configured to perform the following:

6. a range sensor that measures a distance to an object in a predetermined space at each measurement time for each predetermined measurement time interval and outputs three-dimensional point cloud data; a processing unit; An analysis method using an analysis system comprising: an initial background extraction step in which the processing unit extracts one or more stationary objects based on the point cloud data output from the range measurement sensor at each of the measurement times within a predetermined initial background extraction period, and registers, as an initial background, each cell constituting each of the stationary objects in a cell array defined by overlooking the predetermined space and subdividing it into a grid; a moving object extraction step in which the processing unit extracts one or more moving objects by subtracting the initial background from the point cloud data output from the range measurement sensor at the measurement time within a predetermined observation period after the predetermined initial background extraction period; a movable stationary object extraction step in which the processing unit extracts one or more movable stationary objects from the one or more moving objects and redefines each cell constituting each of the movable stationary objects in the cell array as a post-defined background; Including, the processing unit recognizes, as a person, any of the moving objects formed by point cloud data stored in cells other than the cells of the initial background and the post-defined background in the cell array; The movable stationary object refers to an object that moves in response to interaction with a person, In the movable stationary object extraction step, the processing unit a linked movable stationary object extraction step in which a first moving object among the one or more moving objects transitions from a sitting state to a standing state, and then a new moving object is extracted around the first moving object by the moving object extraction step, and then, when a time during which the following formula (5) is continuously satisfied exceeds a predetermined duration threshold within a predetermined new moving object monitoring time from the time when the formula (5) is first satisfied, the new moving object is extracted as a movable stationary object, and each cell constituting the movable stationary object in the cell array is redefined as a post-defined background; This is an analysis method. [Equation 2] In the above formula (5), h(k,t): Height of the new moving object (mm) γ: A given real number between 0 and 200 h sit_ave (p, q): average sitting height of the first moving object (mm)

7. A range finder that measures the distance to an object in a predetermined space at each measurement time for each predetermined measurement time interval and outputs three-dimensional point cloud data; a processing unit; An analysis method using an analysis system comprising: an initial background extraction step in which the processing unit extracts one or more stationary objects based on the point cloud data output from the range measurement sensor at each of the measurement times within a predetermined initial background extraction period, and registers, as an initial background, each cell constituting each of the stationary objects in a cell array defined by overlooking the predetermined space and subdividing it into a grid; a moving object extraction step in which the processing unit extracts one or more moving objects by subtracting the initial background from the point cloud data output from the range measurement sensor at the measurement time within a predetermined observation period after the predetermined initial background extraction period; a movable stationary object extraction step in which the processing unit extracts one or more movable stationary objects from the one or more moving objects and redefines each cell constituting each of the movable stationary objects in the cell array as a post-defined background; Including, the processing unit recognizes, as a person, any of the moving objects formed by point cloud data stored in cells other than the cells of the initial background and the post-defined background in the cell array; The movable stationary object refers to an object that moves in response to interaction with a person, In the movable stationary object extraction step, the processing unit a linear movable stationary object extraction step in which, when an obstructed area appears in the initial background at the same location over a predetermined obstructed area monitoring time, a second moving object located between the range measurement sensor and the obstructed area is identified from among the one or more moving objects, a second linearity evaluation value representing the degree of linearity of the object is calculated for the second moving object, and when the second linearity evaluation value exceeds a predetermined second linearity evaluation value threshold, the second moving object is extracted as a movable stationary object, and each cell constituting the movable stationary object in the cell array is redefined as a later-defined background. This is an analysis method.

8. A range finder that measures the distance to an object in a predetermined space at each measurement time for each predetermined measurement time interval and outputs three-dimensional point cloud data; a processing unit; An analysis method using an analysis system comprising: an initial background extraction step in which the processing unit extracts one or more stationary objects based on the point cloud data output from the range measurement sensor at each of the measurement times within a predetermined initial background extraction period, and registers, as an initial background, each cell constituting each of the stationary objects in a cell array defined by overlooking the predetermined space and subdividing it into a grid; a moving object extraction step in which the processing unit extracts one or more moving objects by subtracting the initial background from the point cloud data output from the range measurement sensor at the measurement time within a predetermined observation period after the predetermined initial background extraction period; a movable stationary object extraction step in which the processing unit extracts one or more movable stationary objects from the one or more moving objects and redefines each cell constituting each of the movable stationary objects in the cell array as a post-defined background; Including, the processing unit recognizes, as a person, any of the moving objects formed by point cloud data stored in cells other than the cells of the initial background and the post-defined background in the cell array; The movable stationary object refers to an object that moves in response to interaction with a person, In the movable stationary object extraction step, the processing unit a highest point slight fluctuation type movable stationary object extraction step in which, when the highest point of a third moving object among the one or more moving objects is located at approximately the same position over a predetermined highest point monitoring time, the third moving object is extracted as a movable stationary object, and each cell constituting the movable stationary object in the cell array is redefined as a post-defined background. This is an analysis method.

9. A range finder that measures the distance to an object in a predetermined space at each measurement time for each predetermined measurement time interval and outputs three-dimensional point cloud data; a processing unit; An analysis method using an analysis system comprising: an initial background extraction step in which the processing unit extracts one or more stationary objects based on the point cloud data output from the range measurement sensor at each of the measurement times within a predetermined initial background extraction period, and registers, as an initial background, each cell constituting each of the stationary objects in a cell array defined by overlooking the predetermined space and subdividing it into a grid; a moving object extraction step in which the processing unit extracts one or more moving objects by subtracting the initial background from the point cloud data output from the range measurement sensor at the measurement time within a predetermined observation period after the predetermined initial background extraction period; a movable stationary object extraction step in which the processing unit extracts one or more movable stationary objects from the one or more moving objects and redefines each cell constituting each of the movable stationary objects in the cell array as a post-defined background; Including, the processing unit recognizes, as a person, any of the moving objects formed by point cloud data stored in cells other than the cells of the initial background and the post-defined background in the cell array; The movable stationary object refers to an object that moves in response to interaction with a person, In the movable stationary object extraction step, the processing unit a voxel number slight variation type movable stationary object extraction step, in which, when a voxel number variation evaluation value representing the degree of variation in the number of voxels constituting the fourth moving object of the one or more moving objects is less than a predetermined voxel number variation evaluation value threshold, the fourth moving object is extracted as a movable stationary object, and each cell constituting the movable stationary object in the cell array is redefined as a post-defined background; This is an analysis method.

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