Object state recognition method and server

Abstract

The present invention relates to the field of laser radar application technology. An embodiment discloses an object state recognition method for using a laser radar to determine the state of a target object in a parking space, including calibrating the laser radar and determining a calibration value, wherein the calibration value is the actual distance from the laser radar to the ground; acquiring multiple frames of radar data; determining whether the target object is in a stable state based on the multiple frames of radar data; and determining the state of the target object based on the target object being in an unstable state. On the other hand, the present application discloses a server for determining the state of a target object in a parking space, including a connection module for connecting to multiple laser radars and receiving multiple laser radar data; a memory for storing the laser radar data and the state of the target object; and a processor for executing a method for identifying the state of a right object. According to at least one embodiment of the embodiments disclosed herein, the state of a parking space in a parking lot can be effectively identified.

Description

Technical Field

[0001] The present invention relates to the field of laser radar application technology, and in particular to a laser radar-based object state recognition method and server. Background Art

[0002] Lidar is a radar system that emits laser beams to detect the position, speed and other characteristic quantities of a target. Its working principle is to emit a detection signal (laser beam) to the target, and then compare the received signal reflected from the target (target echo) with the emitted detection signal. After appropriate processing, relevant information about the target can be obtained, such as the target's distance, direction, height, speed, attitude, and even shape.

[0003] Object state recognition: The recognition of object state is generally to estimate and identify the state of the object according to the video stream or picture, and then derive the current dynamics of the object. At present, object state recognition is generally a combination of image recognition and image processing algorithms. The algorithm is complex, the sensor is expensive, and the recognition rate depends on the image quality and environmental conditions. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a method for identifying an object state.

[0005] In a first aspect, an embodiment of the present invention provides an object state recognition method for using a laser radar to determine the state of a target object in a parking space, calibrating the laser radar, and determining a calibration value, wherein the calibration value is the actual distance from the laser radar to the ground; acquiring multiple frames of radar data; determining whether the target object is in a stable state based on the multiple frames of radar data; and determining the state of the target object based on the target object being in an unstable state.

[0006] In some embodiments, calibrating the laser radar includes: determining whether the laser radar needs to be calibrated; based on the laser radar needing calibration, automatically calibrating or manually calibrating the laser radar; wherein the manual calibration of the laser radar includes writing the calibration value into a fixed storage area of ​​the laser radar.

[0007] In some embodiments, the target object status includes: no target object, target object entering, target object exiting, and target object existing.

[0008] In a second aspect, an embodiment of the present invention provides a server for determining the state of a target object in a parking space, including a connection module for connecting to multiple laser radars and receiving multiple laser radar data; a memory for storing the laser radar data and the state of the target object; and a processor for executing an object state recognition method.

[0009] According to at least one embodiment of the present disclosure, the status of a parking space in a parking lot can be effectively identified. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0011] Figure 1 This is a typical application scenario diagram based on the embodiment of the present application.

[0012] Figure 2 It is an exemplary workflow diagram based on an embodiment of the present application.

[0013] Figure 3 This is an exemplary functional block diagram of a server based on an embodiment of the present application. DETAILED DESCRIPTION

[0014] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only embodiments of a part of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present application. It should be clear that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0015] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0016] The embodiments of the present application provide an object state recognition method and system, so as to realize the monitoring of the parking space state, so as to better control the real-time situation of the parking space in the parking lot. In some embodiments, the parking space monitoring system and method can be applied to underground parking lots, open-air parking lots, mechanical parking lots or smart parking lots in shopping malls, residential areas, scenic spots, roadsides, communities, hotels, and public places. It should be understood that the object state recognition method and system are not limited to the state monitoring of parking spaces, but are also applicable to the state recognition of objects in other spaces. For example, in addition to parking spaces, the space described can be the same as airports, shelves, work desks and other spaces.

[0017] Figure 1 This is a typical application scenario diagram based on the embodiment of this application. Figure 1 As shown, the scene includes multiple laser radars 101-1, 101-2...101-N and multiple parking spaces 202. The laser radar 101 includes a light transmitter that can emit detection light to a specific position of the parking space 202 and receive a reflection signal, and calculate the distance information from the laser radar 101 to the parking space 202 based on the reflection signal. In some embodiments, the specific position can be any point on the parking space 202, such as the center point. The specific position can also be a common point that the vehicle can cover when parked in a parking space. In some embodiments, the light source for emitting the detection light is a laser light source or an LED (light emitting diode) light source.

[0018] In some embodiments, the laser radar 101 can correspond one-to-one with the parking space 202.

[0019] In some embodiments, the server 100 is used to receive distance information acquired by the laser radar 101, wherein the distance information includes but is not limited to any one of the following: the parking space corresponding to the laser radar 101, and the distance information from the laser radar 101 to the corresponding parking space. The server 100 can determine the state of the parking space corresponding to the laser radar based on the distance information. In some embodiments, the server 100 can be a single server or a server group, wherein the server group can be centralized or distributed. The server 100 can be local or remote. The server 100 can also be a cloud platform, including but not limited to any one or a combination of public cloud, private cloud, hybrid cloud and community cloud.

[0020] The embodiment of the present application discloses an object state recognition method for determining the state of a target object in a parking space using a laser radar. In one of the roadside application scenarios, the laser radar is installed at a height of 115 cm, the laser radar is 50 cm away from the edge of the garage, and the angle range with the horizontal plane is 12-20° to ensure that the laser radar can illuminate the light spot to the center. The laser radar is used to illuminate the center position of the parking space and obtain the distance from the laser radar to the ground. Use an infrared camera and high-reflective paper to determine the position of the laser radar's light spot, adjust the horizontal angle of the laser radar installation, ensure that the laser radar's light spot is in the center of the garage, and record the actual distance (hypotenuse distance) from the laser radar to the ground.

[0021] The laser radar is calibrated to determine a calibration value, wherein the calibration value is the true distance from the laser radar to the ground. Calibrating the laser radar includes: determining whether the laser radar needs to be calibrated; based on the laser radar needing to be calibrated, automatically calibrating or manually calibrating the laser radar; wherein manually calibrating the laser radar includes writing the calibration value into a fixed storage area of ​​the laser radar.

[0022] Acquire multiple frames of radar data. Continuously collect N (flexibly adjustable interval is 30-60 frames) frames of data, and judge different parking spaces based on data volatility. Mainly distinguish three types of parking spaces, that is, data fluctuations within -15-15cm, -30-30cm, and -60-60cm. Different calibration methods are used according to different situations.

[0023] When the data fluctuation is within -15-15cm, the N frames of data will be averaged and assigned to the calibration value. At this time, the data is relatively stable, indicating that the current parking space is normal. When the data fluctuation is within -30-30cm, the N frames of data will be sorted and the minimum 10 frames and the maximum 10 frames will be subtracted. Then the remaining data will be averaged. At this time, it is, for example, a highly reflective parking space floor with epoxy paint. When the data fluctuation is within -60-60cm, the N frames of data will be sorted, and then the remaining data will be histogrammed. At this time, it is a reflective parking space floor after rain. If the absolute value exceeds the range of 60cm, the signal strength received by the laser radar is less than 100 or the received signal strength is very large and close to overexposure, it is considered that the laser radar is faulty or there is a highly reflective black object very close to the radar. In this case, calibration is impossible and an alarm message is given.

[0024] Determine whether the target object is in a stable state based on the multiple frames of radar data; and determine the state of the target object based on whether the target object is in an unstable state.

[0025] Wherein, determining whether the target object is in a stable state based on the multi-frame radar data includes: determining the range of the multi-frame radar data, wherein the range is the difference between the maximum value and the minimum value in the multi-frame radar data; determining whether the range is greater than a first threshold; determining that the target object is in an unstable state based on the range being greater than the first threshold; determining that the target object is in a stable state based on the range being less than the first threshold. In one embodiment, the range of 20 consecutive frames of data is calculated. If the range is less than 30cm, it is considered that the current state is stable. The stable state is divided into two categories. One category is that there is no object in the parking space, and the data at this time is in a stable state; the other category is that there is a stationary object in the parking space, which is reflected in the difference between the mean of the current 20 frames of data and the ground being more than 40cm, and the data fluctuation is also relatively small.

[0026] In one embodiment, determining that the target object is in a stable state includes: determining a distance difference between an average of the multiple frames of radar data and the calibration value; determining that the stable state is that there is no target object in the parking space based on the distance difference being less than a first distance threshold; and determining that there is a target object in the parking space and the target object is stationary based on the distance difference being greater than the first distance threshold.

[0027] In the embodiments of the present application, the target object states generally include: no target object, target object entering, target object exiting, and target object existing.

[0028] The target object state includes: determining, based on the multi-frame radar data, a slope of the multi-frame radar data, a slope sum, a first distance difference between the multi-frame radar data and the calibration value, and a second distance difference between the multi-frame radar data and the multi-frame radar data at a previous moment; determining a previous object state of the target object at a previous moment; and determining the target object state based on the slope, the slope sum, the distance difference, and the previous object state.

[0029] Determining the target object state includes: determining the target object state as a no-target object state based on the previous object state being a no-target object state and the absolute value of the first distance difference being less than a second distance threshold;

[0030] Based on the fact that the previous object state is that there is no target object, the absolute value of the first distance difference is greater than the third distance threshold and the slope sum is less than the first slope sum threshold, the target object state is determined to be a target object entry state; based on the fact that the previous object state is that there is no target object, the absolute value of the first distance difference is greater than the third distance threshold and the slope sum is greater than the second slope sum threshold, the target object state is determined to be a target object exit state; based on the fact that the absolute value of the slope sum is less than the second slope sum threshold and the absolute value of the second distance difference is less than the second distance threshold, the target object state is determined to be that there is a target object.

[0031] In one embodiment, the state of an object is determined: if the previous state is a steady state with no object, and the absolute value of the difference between the distance data detected by the current laser radar and the calibration value (the distance value between the laser radar and the ground) is less than 30 cm, it is considered that there is no object; if the previous state is a steady state with no object, and the absolute value of the difference between the distance data detected by the current laser radar and the calibration value (the distance value between the laser radar and the ground) is greater than 80 cm and the sum of the slopes is less than -30, it is considered that an object has entered; if the previous state is a steady state with no object, and the absolute value of the difference between the current data and the calibration value (the distance value between the laser radar and the ground) is greater than 80 cm and the sum of the slopes is less than -30, it is considered that an object has entered; If the absolute value of the difference is greater than 80 and the sum of the slopes is greater than 30, it is considered that an object has exited; if the absolute value of the sum of the slopes is less than 30 and the absolute value of the difference between the current data and the data value at the previous moment is less than 30, it is considered that an object has exited; if the absolute value of the sum of the slopes is greater than 100 and the signal strength of the lidar is less than 70, it is considered that the current state is uncertain; if the previous moment was a steady state, the difference between the current moment and the calibration value (ground distance value) is greater than 40 and the slope of the next moment is positive and greater than 30, it is considered that this state is that the object enters quickly and does not stop to exit.

[0032] In one embodiment, the target object state includes: based on the previous object state being no target object and the slope being less than a first slope threshold, determining the target object state to be an object entering state; based on the previous object state being a target object existing and the slope being less than a second slope threshold, determining the target object state to be an object exiting state; based on the previous object state being a target object entering and the absolute value of the slope being less than a third slope threshold, determining the target object state to be a target object existing; based on the previous object state being a target object entering and the slope being greater than a third slope threshold and less than a fourth slope threshold, determining the target object to be a target object entering state; based on the previous object state being a target object entering and the slope being greater than a third slope threshold and less than a fourth slope threshold, determining the target object to be a target object entering state; based on the previous object state being a target object entering and the slope being less than a third slope threshold and less than a fourth slope threshold, determining the target object to be a target object entering state. The target object is determined to be in a target exit state based on the negative value of the slope threshold or the slope being greater than a fourth slope threshold; the target object state is determined to be a no-target object state based on the previous object state being a target object exit and the slope being less than the negative value of the fourth slope; the target object is determined to be in a target exit state based on the previous object state being a target object exit and the slope being less than the negative value of the third slope and greater than the negative value of the fourth slope; the target object state is determined to be a target object entry state based on the previous object state being a target object exit and the slope being less than the negative value of the third slope and greater than the negative value of the fourth slope; the target object state is determined to be a target object entry state based on the previous object state being a target object exit and the slope being greater than the third slope.

[0033] In one embodiment, when the state at the previous moment was no object, if the current slope is less than -40, it is considered that an object is currently entering, the state is set to the object entering state, and the current value is recorded; when the state at the previous moment was an object, if the current slope is greater than 40, it is considered that an object is currently exiting, the state is set to the object exiting state, and the current value is recorded; when the state at the previous moment was object entering, if the absolute value of the current slope is less than 5, it is considered that the current state is an object existing state, if the slope is greater than 5 and less than 15, it is considered that the current state is continuing to enter, and other states are considered to be object exiting states; when the state at the previous moment was object exiting, if the absolute value of the current slope is less than 5, it is considered that the current state is an object not existing state, if the slope is less than -15, it is considered that the current state is continuing to exit, and other states are considered to be object entering states; other states are marked as unknown states.

[0034] The present application also discloses an object state recognition system for determining the state of a target object in a parking space using a laser radar. Figure 3 As shown, it includes: a calibration module 301, used to calibrate the laser radar and determine a calibration value, wherein the calibration value is the real distance from the laser radar to the ground; a radar data acquisition module 302, used to acquire multiple frames of radar data; a stable state judgment module 303, used to determine whether the target object is in a stable state based on the multiple frames of radar data; a target object state judgment module 304, used to determine the state of the target object based on that the target object is in an unstable state.

[0035] The calibration module is further used to: determine whether the laser radar needs to be calibrated; based on the need for calibration of the laser radar, automatically calibrate or manually calibrate the laser radar; wherein the manual calibration of the laser radar includes writing the calibration value into a fixed storage area of ​​the laser radar.

[0036] The stable state judgment module is further used to: determine the range of the multi-frame radar data, wherein the range is the difference between the maximum value and the minimum value in the multi-frame radar data; determine whether the range is greater than a first threshold; based on the range being greater than the first threshold, determine that the target object is in an unstable state;

[0037] Based on the range being less than a first threshold, it is determined that the target object is in a stable state.

[0038] The target object state module is used to: determine the slope of the multi-frame radar data, the slope sum, the first distance difference between the multi-frame radar data and the calibration value, and the second distance difference between the multi-frame radar data and the multi-frame radar data at the previous moment based on the multi-frame radar data; determine the previous object state of the target object at the previous moment; and determine the target object state based on the slope, the slope sum, the distance difference and the previous object state.

[0039] The embodiment of the present application also discloses a server for determining the state of a target object in a parking space, a connection module for connecting to multiple laser radars and receiving multiple laser radar data; a memory for storing the laser radar data and the state of the target object; and a processor for executing methods such as object state judgment.

[0040] In the embodiment of the present application, the slope is the difference between adjacent data, and the slope sum is the sum of the differences between each adjacent data of 20 frames of data. It should be understood that the definitions of the slope and the slope sum and the corresponding difference values ​​are not limited thereto, and can be the common meanings in the art.

[0041] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. An object state recognition method for determining the state of a target object in a parking space using a laser radar, characterized in that: Calibrate the laser radar to determine a calibration value, wherein the calibration value is a true distance from the laser radar to the ground; Get multiple frames of radar data; Determining whether the target object is in a stable state based on the multiple frames of radar data; Based on the target object being in an unstable state, determining the state of the target object; The determining whether the target object is in a stable state based on the multi-frame radar data includes: determining the range of the multi-frame radar data, wherein the range is the difference between the maximum value and the minimum value in the multi-frame radar data; determining whether the range is greater than a first threshold; determining that the target object is in an unstable state based on the range being greater than the first threshold; determining that the target object is in a stable state based on the range being less than the first threshold; the determining the state of the target object includes: determining the slope of the multi-frame radar data, the slope sum, the first distance difference between the multi-frame radar data and the calibration value, and the second distance difference between the multi-frame radar data and the multi-frame radar data at the previous moment based on the multi-frame radar data; determining the previous object state of the target object at the previous moment; determining the target object based on the slope, the slope sum, the distance difference and the previous object state. object state; based on the previous object state being no target object and the absolute value of the first distance difference being less than the second distance threshold, determining that the target object state is no target object state; based on the previous object state being no target object, the absolute value of the first distance difference being greater than the third distance threshold and the slope sum being less than the first slope sum threshold, determining that the target object state is a target object entry state; based on the previous object state being no target object, the absolute value of the first distance difference being greater than the third distance threshold and the slope sum being greater than the second slope sum threshold, determining that the target object state is a target object exit state; based on the absolute value of the slope sum being less than the second slope sum threshold and the absolute value of the second distance difference being less than the second distance threshold, determining that the target object state is a target object presence; wherein the slope is the difference between adjacent data, and the slope sum is the sum of the differences between each adjacent data of 20 frames of data.

2. The method according to claim 1, characterized in that Calibrating the laser radar includes: determining whether the laser radar needs to be calibrated; based on the laser radar needing to be calibrated, automatically calibrating or manually calibrating the laser radar; wherein the manual calibration of the laser radar includes writing the calibration value into a fixed storage area of ​​the laser radar.

3. The method according to claim 1, characterized in that The laser radar is used to illuminate the center position of the parking space and obtain the distance from the laser radar to the ground.

4. The method according to claim 1, characterized in that Determining that the target object is in a stable state includes: determining a distance difference between an average of the multiple frames of radar data and the calibration value; determining that the stable state is that there is no target object in the parking space based on the distance difference being less than a first distance threshold; and determining that there is a target object in the parking space and the target object is stationary based on the distance difference being greater than the first distance threshold.

5. The method according to claim 4, characterized in that The target object states include: no target object, target object entering, target object exiting and target object existing.

6. The method according to claim 5, characterized in that The determining of the target object state comprises: determining the target object state as an object entering state based on the previous object state being no target object and the slope being less than a first slope threshold; determining the target object state as an object exiting state based on the previous object state being a target object entering and the slope being less than a second slope threshold; determining the target object state as a target object existing state based on the previous object state being a target object entering and the absolute value of the slope being less than a third slope threshold; determining the target object state as a target object entering state based on the previous object state being a target object entering and the slope being greater than a third slope threshold and less than a fourth slope threshold; determining the target object as a target object entering state based on the previous object state being a target object entering and the slope being less than a third slope threshold. The target object is determined to be in a target exit state based on the negative value of the previous object state or the slope being greater than a fourth slope threshold; the target object state is determined to be a no-target object state based on the previous object state being a target object exit and the slope being less than the negative value of the fourth slope; the target object is determined to be in a target exit state based on the previous object state being a target object exit and the slope being less than the negative value of the third slope and greater than the negative value of the fourth slope; the target object state is determined to be a target object entry state based on the previous object state being a target object exit and the slope being less than the negative value of the third slope and greater than the negative value of the fourth slope; the target object state is determined to be a target object entry state based on the previous object state being a target object exit and the slope being greater than the third slope.

7. A server for determining the state of a target object in a parking space, characterized in that: A connection module, used to connect with multiple laser radars and receive multiple laser radar data; a memory, used to store the laser radar data and the target object state; A processor, configured to execute the method according to any one of claims 1 to 6.

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