Method and apparatus for simulating the trajectory of an object

Abstract

The present application provides a method for simulating an action track of an object, which comprises the steps of: extracting a plurality of frames of images from a video; determining a plurality of contour coordinates of a first tracking object in each image according to a first feature of the first tracking object; identifying a first frame shape according to the contour coordinates; generating a vector according to a relative relationship between a reference coordinate and a center point coordinate of the first frame shape, and simulating an action track of a target object according to the vector, wherein the target object is associated with the first tracking object; and numerizing the action track and plotting the action track to a corresponding coordinate of a data visualization graph. The present application also provides a device for simulating an action track of an object.

Description

Technical Field

[0001] This invention relates to image tracking technology; more specifically, it relates to a method and apparatus for simulating the trajectory of a specific object. Background Technology

[0002] With the development of artificial intelligence, image recognition and object detection technologies have also improved significantly (such as the YOLO (You Only Look Once) object detection algorithm), and have been applied in various fields such as autonomous driving, smart healthcare, and facial recognition. Through machine learning techniques, AI can continuously learn to recognize the features of specific objects using a training set, thereby quickly and accurately capturing target objects in different images and continuously tracking them as they move.

[0003] While current object detection technology can already perform tasks such as identifying vehicles on the road, classifying vehicle types, and continuously tracking vehicles as they move, existing object tracking technologies are difficult to apply to tracking targets with non-fixed shapes (such as air currents or water columns). This is because AI cannot recognize the shape of the target, struggles to learn its features through training sets, or even sense its presence in images. Summary of the Invention

[0004] One object of the present invention is to provide a method and apparatus for simulating the trajectory of an object, which can track the target object and then simulate the trajectory or range of its action (such as a jet of water or airflow).

[0005] One object of the present invention is to provide a method or apparatus for simulating the trajectory of an object, which can easily estimate the area in an image that has been affected by the object and present it to the user.

[0006] The method for simulating the trajectory of an object includes: extracting multiple frames from a video; determining multiple contour coordinates of the first tracked object in each frame based on a first feature of the object; identifying a first frame based on these contour coordinates; generating a vector based on the relative relationship between reference coordinates and the center point coordinates of the first frame; simulating the trajectory of a target object associated with the first tracked object based on this vector; and quantifying the trajectory and plotting it on the corresponding coordinates of a data visualization. This method allows for the simulation of the trajectory (e.g., the direction and range of air ejection) of tangible objects (e.g., gloves, spray guns, etc.) by tracking them, thereby estimating the distribution range of intangible substances (e.g., air, water, etc.). Users can also easily understand the area already affected (e.g., cleaned) and the area yet to be affected within a fixed region based on the data visualization.

[0007] An apparatus for simulating the trajectory of an object includes at least one memory and at least one processor coupled to the memory, the processor being configured to: determine and locate a plurality of contour coordinates of the first tracked object in each image based on a first feature of the first tracked object; locate a first frame around the contour coordinates of the first tracked object based on the contour coordinates of the first tracked object; generate a vector based on a line connecting reference coordinates and coordinates toward the center point of the first frame, and simulate the trajectory of a target object based on the vector, wherein the target object may be identical to the first tracked object or directly or indirectly connected to the first tracked object; and quantify the trajectory and plot it on the corresponding coordinates of a data visualization. With this apparatus, the trajectory (e.g., direction and range of air ejection) of a tangible object (e.g., a glove, a spray gun, etc.) can be simulated by tracking the object, thereby estimating the distribution range of intangible substances (e.g., air, water, etc.). Furthermore, users can easily understand the areas that have been treated (e.g., cleaned) and the areas that are yet to be treated in a fixed area based on this data visualization. Attached Figure Description

[0008] Figure 1 This is a flowchart illustrating the steps of a method for simulating the trajectory of an object according to an embodiment of the present invention.

[0009] Figure 2A This is a schematic diagram of image capture according to an embodiment of the present invention.

[0010] Figure 2B This is a schematic diagram of a captured image including a region of interest (ROI) according to an embodiment of the present invention.

[0011] Figure 3 This is a schematic diagram of an image and its recognition result according to another embodiment of the present invention.

[0012] Figure 4 This is a schematic diagram of the simulated object action trajectory according to an embodiment of the present invention.

[0013] Figure 5A This is a schematic diagram of the action trajectory values ​​according to an embodiment of the present invention.

[0014] Figure 5B This is a schematic diagram of the action trajectory values ​​according to another embodiment of the present invention.

[0015] Figure 5C This is a schematic diagram of a data visualization according to an embodiment of the present invention.

[0016] Figure 6 This is a schematic diagram of the simulated object action trajectory when multiple target objects are included, according to another embodiment of the present invention.

[0017] Figure 7 This is a schematic diagram of an apparatus for simulating the trajectory of an object according to another embodiment of the present invention.

[0018] The reference numerals in the attached figures are explained as follows:

[0019] 100…Step-by-step diagram

[0020] 101…steps

[0021] 103… steps

[0022] 105…steps

[0023] 107…steps

[0024] 108…steps

[0025] 109…steps

[0026] 201…images

[0027] 202…Region of Interest (ROI)

[0028] 203…First Tracking Item

[0029] 204… minus part

[0030] 205…First frame

[0031] 207… Reference coordinates

[0032] 303…Reflection

[0033] 305… Error identification box

[0034] 307… Correct Identification Box

[0035] 403… Coordinates of the center point of the first frame

[0036] 405… vector

[0037] 407a… coordinates

[0038] 407b…coordinates

[0039] 407c… coordinates

[0040] 500… Data Visualization Chart

[0041] 503…Reference Indicator

[0042] 503a…Instant Coverage

[0043] 503b…Trajectory Indicator

[0044] 700… device

[0045] 710… Memory

[0046] 720… processor

[0047] 721…Image Capture Unit

[0048] 722…Image Recognition Module

[0049] 723…Computational Unit

[0050] 725… Data Conversion Unit

[0051] 730… Display Unit

[0052] 740…Image source

[0053] T…Action Trajectory

[0054] R…range

[0055] A…Expansion Angle

[0056] V N …variable Detailed Implementation

[0057] The terms “about,” “approximately,” or “substantially” as used herein include the value and the average value within an acceptable range of deviations from a particular value as determined by one of ordinary skill in the art, taking into account the measurement under discussion and the specific amount of error associated with the measurement (i.e., limitations of the measurement system). For example, “about” may mean within one or more standard deviations of the value, or within ±30%, ±20%, ±10%, ±5%. Furthermore, the terms “about,” “approximately,” or “substantially” as used herein may be chosen to select a more acceptable range of deviations or standard deviations depending on the optical, etched, or other properties, and may not apply to all properties with a single standard deviation.

[0058] It should be understood that although the terms “first,” “second,” “third,” etc., may be used herein to describe various elements, components, regions, layers, and / or parts, these elements, components, regions, and / or parts should not be limited by these terms. These terms are used only to distinguish one element, component, region, layer, or part from another. Therefore, the “first element,” “component,” “region,” “layer,” or “part” discussed below may be referred to as a second element, component, region, layer, or part without departing from the teachings of this document.

[0059] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having meanings consistent with their meanings in the context of the relevant technology and this invention, and will not be interpreted as having idealized or overly formal meanings unless expressly defined herein.

[0060] This invention relates to how to simulate the trajectory of a target object (e.g., a spray gun) to assess the effective range of indeterminate or invisible substances (e.g., air, water). For example, when cleaning equipment in a factory, tools such as water jets or air spray guns may be used. While current image recognition technologies can identify tangible objects (e.g., spray guns), they struggle to identify the distribution range of the water jet or airflow emitted by the spray gun. Therefore, this invention aims to provide a trajectory for the airflow or water jet emitted by a spray gun, thereby assessing the cleanliness of the equipment and quickly distinguishing between cleaned and uncleaned areas. The invention will be described in detail below with reference to various embodiments. It should be understood that these embodiments are merely illustrative examples to enable those skilled in the art to understand the invention. Modifications to the following illustrative embodiments can be made without departing from the spirit of the invention, and such modifications are intended to be covered by this invention.

[0061] refer to Figure 1 and Figure 2A This invention describes a method for simulating the trajectory of an object. In step 101, the method includes capturing multiple frames 201 from a video. The video is a scene recording a target object (e.g., a spray gun) and the area where the target object may act (e.g., the equipment to be cleaned). In a preferred embodiment, the multiple frames 201 can be continuously captured images 201. In other embodiments, the images 201 can also be captured at intervals. For example, images 201 are captured from the video every 0.5 seconds over a period of time. Depending on the requirements, the interval for capturing images 201 can also be longer (e.g., per second) or shorter (e.g., every 0.3 seconds). The length of the interval may affect the accuracy of the simulated object trajectory. For example, when the interval is long, the temporal difference between the multiple frames 201 is large, and the simulated trajectory also has large discontinuities, thus being less accurate.

[0062] refer to Figure 2BContinuing with this embodiment, capturing multiple frames 201 from a video can further include capturing only the image of a user-defined region of interest (ROI) 202 (shown as a diagonal line). For example, when a user wants to know the current cleanliness of a device, they can capture only the image 201 covering that part of the device in the video. Capturing the ROI 202 can further include performing morphological processing on the captured ROI 202, such as dilating the captured ROI 202 to increase its area. This processing avoids the possibility of missing objects that are too close to or slightly beyond the edge of the ROI 202. Furthermore, logical operations can be used to process the image 201 to obtain an ROI 202 of a specific shape; for example, a portion of the image 204 can be subtracted from the image 201 to obtain a hollow frame-shaped ROI 202.

[0063] It should be noted that the image 201 can be a file of any format (such as a JPEG file), and image processing (such as dimensionality reduction) can be performed on the image 201 to improve the efficiency of image processing and reduce the required performance. Alternatively, the image 201 can be converted from an RGB model to an HSV model to facilitate subsequent image recognition based on color, and users can more intuitively adjust colors when setting color-related features. The foregoing descriptions of the format and image processing of the image 201 are illustrative and, without conflicting with the content of this invention, the image 201 can be any other format or undergo any processing.

[0064] refer to Figure 2AContinuing with an embodiment of the present invention, a method for simulating the trajectory of an object is described. In step 103, the method includes determining the contour coordinates of a first tracked object 203 in the image 201 based on a first feature. The first tracked object 203 may be a shaped object such as a glove, and may have a specific first feature (e.g., red). Based on this first feature, the first tracked object 203 can be identified from the image 201 (e.g., through image color filtering technology), and multiple contour coordinates of the location of the first tracked object 203 can be obtained. In different embodiments, the first feature can also be set by the user (e.g., the user can set its first feature to different colors such as blue or green). In step 105, the method can identify a first frame 205 surrounding these contour coordinates (i.e., the area surrounded by the first frame 205 can cover all contour coordinates). It should be noted that in this embodiment, the first frame 205 has a minimum area that can surround these contour coordinates. However, in different embodiments, the first frame 205 may also have different area ranges (e.g., having twice the area that can surround these contour coordinate ranges). This invention does not impose any limitations.

[0065] Another embodiment of the present invention provides a method for simulating the trajectory of an object, which may include performing morphological image processing (e.g., erosion, dilation, etc.) on the contour coordinates of the identified first tracked object 203 to eliminate noise, restore the broken contour of the identified object, etc., so that these contour coordinates are closer to the true shape of the first tracked object 203. Subsequently, the area of ​​the contour coordinates can be calculated and compared with the estimated area of ​​the first tracked object 203 (i.e., the area of ​​the first tracked object 203 presented in the image 201), thereby excluding contour coordinates outside the error range (e.g., ±5%). Similarly, the area of ​​the first frame shape 205 can be calculated and compared with the estimated area of ​​the first frame shape 205 (i.e., the area of ​​the frame surrounding the first tracked object 203 presented in the image 201), thereby identifying the first frame shape 205 outside the error range (e.g., ±5%), and thus excluding the contour coordinates corresponding to the first frame shape 205. (See reference...) Figure 3 Specifically, Figure 3The left side shows an example image of image 201, and the right side shows a schematic diagram of the result after image 201 is identified. When there are smooth objects such as metal in the scene of image 201, the first tracked object 203 may generate a reflection 303 on the smooth object. After identification, image 201 may simultaneously identify the first tracked object 203 and the reflection 303 as the first tracked object 203 (the incorrect identification box 305 in the right figure shows the incorrect identification result of the reflection 303 being mistakenly identified as the first tracked object 203, and the correct identification box 307 shows the correct identification result of the first tracked object 203 being correctly identified), thus affecting the identification result. Since the reflection of an object (as shown in the error identification box 305) usually does not have the same complete shape as the original object (as shown in the correct identification box 307), by excluding the contour coordinates whose area is outside the error range in this embodiment, the chance of misjudging the reflection 303 of the first tracked object 203 as the first tracked object 203 can be avoided.

[0066] refer to Figures 2A-2B and Figure 4 Continuing with an embodiment of the present invention, a method for simulating the trajectory of an object is described. In step 107, the method includes generating a vector 405 based on the relative relationship between reference coordinates 207 and the center point coordinates 403 of the first frame 205. In this embodiment, the reference coordinates 207 are pre-set fixed coordinates (e.g., the center point coordinates of an image), and the vector 405 is generated by connecting the reference coordinates 207 to the center point coordinates 403 of the first frame 205. The target object can be a first tracking object 203 (e.g., the same spray gun), or when the target object is difficult to identify and track (e.g., its features are not obvious or it is obscured), the target object can be an object directly or indirectly connected to the first tracking object 203 (e.g., the first tracking object 203 is a glove, and the target object is a spray gun held in the glove). With this configuration, even when the target object is difficult to identify and track (e.g., when a cleaner holds the spray gun in their hand, the spray gun itself is small and is obstructed by the cleaner's hand), the target object can still be tracked by identifying and tracking the first tracking object 203 that is directly or indirectly connected to it.

[0067] It should be noted that although the reference coordinate 207 is a pre-set fixed coordinate in this embodiment, in different embodiments, the center point coordinate 207 can also be determined by the second tracked object. The second tracked object includes a second feature (e.g., blue), and this second feature is different from the first feature to avoid confusion during the identification process. The second feature of the second tracked object can also be set by the user. Based on the second feature, the second tracked object can be identified from the image 201, multiple contour coordinates of the second tracked object can be obtained, and a second frame shape can be generated around these contour coordinates. The center point of the second frame shape can be used as the reference coordinate 207, and a vector 405 can be generated by connecting the reference coordinate 207 to the center coordinate 403 of the first frame shape 205.

[0068] The following is for reference Figure 4 The method for simulating the trajectory of an object in this embodiment will continue to be explained. Step 108 includes simulating the trajectory T of the target object based on the vector 405. In this embodiment, the simulated trajectory T of the target object includes not only the vector 405 but also trajectory parameters, which can be set by the user. The vector 405 is associated with the basic direction of action of the target object, while the trajectory parameters are associated with the range of the trajectory T. For example, assuming the trajectory T is a fan-shaped range, the trajectory parameters can include the range R of the target object's action (e.g., the straight-line distance that air or water sprayed by a spray gun can reach, i.e., the radius of the fan), the dispersion angle A (i.e., the central angle of the fan), etc. The vector 405 can initially simulate the direction of action of the target object, and combined with the trajectory parameters (range R, dispersion angle A, etc.), the simulation of the target object's trajectory can be made more accurate. It should be noted that although the trajectory T is a fan-shaped range in this embodiment, in different embodiments, the trajectory T can also be simulated as other shapes (e.g., triangles), and this invention does not limit this.

[0069] The following is based on Figure 4 , Figure 5A , Figure 5B and Figure 5C Continuing with an embodiment of the present invention, a method for simulating the trajectory of an object is described. Step 109 includes quantifying the trajectory T and plotting it to the corresponding coordinates of the data visualization graph 500. (See reference...) Figure 5ABased on the foregoing, the trajectory T of the target object can be simulated. Numericalizing the trajectory T can include, for example, assigning the same numerical value to each coordinate of the trajectory T distribution within the same frame image 201 (i.e., each point within the sector distribution has the same value). For instance, coordinates 407a, 407b, and 407c can be assigned the same numerical value (e.g., 10). After multiple frames of images 201 undergo the same processing, since the distribution range of the trajectory T in each frame image 201 may differ, the variable V representing the accumulated values ​​of each point coordinate will change. n The value of the coordinate at that point is accumulated with the temporal sequence of each frame of image 201. Therefore, corresponding to the distribution of the action trajectory T in multiple frames of image 201, the variable V at each coordinate point... n It can accumulate values ​​of different sizes (e.g.) Figure 5A In the middle, the overlapping portion of the two action trajectories T has a variable V with a value of 20. n Furthermore, the degree to which each coordinate point is affected by the target object can be presented through data visualization graph 500.

[0070] refer to Figure 5B In different embodiments, different coordinate points of the action trajectory T distribution in image 201 can also be assigned different numerical values. For example, when a water column or airflow is sprayed from the nozzle, it will be affected by air resistance and gravity, and the distribution density will decrease with increasing distance. Therefore, coordinates closer to the target object can be assigned larger values ​​(e.g., 40), and coordinates farther away from the target object may be assigned successively decreasing values ​​(i.e., coordinate 407a can be assigned a value greater than coordinate 407b, and coordinate 407b can be assigned a value greater than coordinate 407c). Similarly, after performing the same processing on multiple images 201, each coordinate point will accumulate its own value, thereby simulating, for example, when the target object is a water column spray gun, the degree of water column effect on the area will decrease with increasing distance. It should be noted that the aforementioned settings for numerical values ​​are illustrative, and other different numerical settings are possible without departing from the spirit of the present invention. The present invention does not impose any limitations.

[0071] The data visualization chart 500 refers to a method of presenting numerical values ​​graphically using colors, blocks, lines, etc., as shown in the reference. Figure 5C In this embodiment, the value of each coordinate point can be presented in the form of a heatmap based on the magnitude of the numerical value (i.e., the coordinate point with a higher degree of influence from the target object is closer to red on the heatmap, while the coordinate point with a lower degree of influence is closer to purple). It should be noted that although the numerical value is presented in the form of a heatmap in this embodiment, other types of data visualization graphs 500 (such as contour maps) can also be used in different embodiments, and the present invention does not limit this.

[0072] refer to Figure 5C Continuing with the description of the method for simulating the trajectory of an object according to an embodiment of the present invention, in this embodiment, the data visualization 500 may only present the area of ​​ROI 202. For example, when the device that the user wants to clean appears as a frame-shaped area in the image, the data visualization 500 may only present the color distribution of the frame-shaped area to help the user quickly understand the cleaning status of the device. In addition, the data visualization 500 may further include a reference index 503, which may include an instantaneous coverage rate 503a and a trajectory index 503b of the trajectory T. Specifically, the area covered by the trajectory T on the data visualization 500 (i.e., the area that has been affected by the target object) is calculated and divided by the total area of ​​the image 201 or ROI 202 to obtain the instantaneous coverage rate 503a of the trajectory T; the total numerical value of the trajectory T on the data visualization 500 is calculated and divided by the total area of ​​the image 201 or ROI 202 to obtain a trajectory index 503b of the trajectory T. Since the instantaneous coverage 503a and the trajectory indicator 503b change over time and are related to the distribution of the action trajectory T, users can use these reference indicators 503 to determine the instantaneous status of the target object's action. It should be noted that the aforementioned description of the reference indicator 503 is merely illustrative. In different embodiments, the reference indicator 503 may also include other reference indicators 503 that can provide reference information related to the action trajectory T, such as the cumulative action time of the action trajectory T.

[0073] refer to Figure 6 In different embodiments, the same frame of image 201 may include multiple first tracking objects 203 having the same first feature (e.g., red). Based on this first feature, these first tracking objects 203 can be identified from image 201, and the contour coordinates of multiple groups of these first tracking objects 203 can be obtained (the contour coordinates of the same group correspond to the same first tracking object 203), and multiple first frames 205 are generated around these first contour coordinates (the contour coordinates of the same group correspond to one first frame 205). In this embodiment, multiple vectors 405 can be generated by connecting reference coordinates 207 to the center point coordinates of these first frames 205, and multiple action trajectories T can be generated by these vectors 405, thereby simulating the situation where multiple target objects act simultaneously. Specifically, when monitoring the cleaning status of equipment, there may be situations where multiple cleaning tasks are performed simultaneously (e.g., multiple cleaning personnel simultaneously spray water jets with spray guns). With this configuration, the action trajectories T of the multiple spray guns can be simulated simultaneously to understand the real-time cleaning status of the equipment.

[0074] refer to Figure 7An apparatus 700 for simulating the trajectory of an object according to another embodiment of the present invention is described, wherein the apparatus includes a memory 710, a processor 720 coupled to the memory, and a display unit 730. The memory 710 stores a plurality of instructions, and when these instructions are executed, the processor 720 is configured to perform the method for simulating the trajectory of an object as described in any of the preceding embodiments. Specifically, the processor 720 includes an image capturing unit 721, an image recognition module 722, a calculation unit 723, and a data conversion unit 725. The capturing unit 721 captures multiple frames 201 from an image source 740 (i.e., a video containing the target object and its area of ​​effect) and transmits them to the image recognition module 722. The image recognition module 722 determines the contour coordinates of the first tracked object 203 from the captured images 201 and identifies a first frame shape 205. Subsequently, the image recognition module 722 transmits the recognition result to the calculation unit 723. The calculation unit 723 generates a vector 405 based on the relative relationship between the first frame 205 identified by the image recognition module 722 and the reference coordinates 207. Using the action trajectory parameters and the vector 405, the calculation unit 723 can simulate the action trajectory T of the target object in each frame of image 201 and quantify the action trajectory T. Here, the variable V represents the cumulative value of each coordinate point. N It can be stored in a temporary register, and the variable V changes with the timing of multiple frames of image 201. N The continuous accumulation calculation unit 723 quantifies the result of the action trajectory T. The data conversion unit 725 converts the variable V representing each coordinate point... N The numerical values ​​are visualized (e.g., represented by colors), so that each coordinate point has its own image features (e.g., color), and the signal is transmitted to the display unit 730 for presentation to the user.

Claims

1. A method for simulating the trajectory of an object, comprising: Extracting multiple frames from a video; In each of the images, multiple contour coordinates of the first tracked object are determined based on a first feature of the first tracked object; A first frame shape is identified based on the contour coordinates; A vector is generated based on the relative relationship between a reference coordinate and the coordinates of the center point of the first frame, and a trajectory of a target object is simulated based on the vector, wherein the target object is related to the first tracking object. The trajectory of action is quantified and plotted on the corresponding coordinates of a data visualization plot; Determine the center point coordinates of a second tracking object, wherein the second tracking object has a second feature that is different from the first feature of the first tracking object; as well as The coordinates of the center point of the second tracked object are set as the reference coordinates.

2. The method of claim 1, wherein the target object is the same as the first tracking object or is directly or indirectly connected to the first tracking object.

3. The method of claim 1, wherein the image capturing step includes setting a desired monitoring area range so as to capture only the desired monitoring area range when capturing the video; the step of drawing to the data visualization map includes generating only the data visualization map within the desired monitoring area range.

4. The method of claim 3, wherein capturing the image further comprises expanding the area to be monitored to capture a rough area to be monitored.

5. The method of claim 1, wherein determining the contour coordinates of the first tracked object in the image includes dilating and eroding the contour of the first tracked object.

6. The method of claim 1, wherein the step of simulating the action trajectory comprises simulating the action trajectory based on an action trajectory parameter and the vector.

7. The method of claim 1, further comprising: Calculate the area of ​​a contour based on the contour coordinates; The contour area is compared with an actual area of ​​the first tracked object to produce a comparison result; and The contour coordinates that are outside the error range are excluded based on the comparison results.

8. The method of claim 1, further comprising: Calculate the area of ​​the first frame shape; Compare the area of ​​the first frame with an estimated area of ​​the first frame to produce a comparison result; and Based on the comparison result, the first frame shape that is outside the error range is excluded, and the contour coordinates that are outside the error range corresponding to the first frame shape are also excluded.

9. The method of claim 1, wherein the step of plotting to the data visualization further comprises: Associate this action trajectory with at least one reference indicator; as well as This reference indicator is incorporated into the data visualization.

10. The method of claim 1, wherein the step of numerating the action trajectory includes setting the same or different values ​​according to different position coordinates of the action trajectory.

11. The method of claim 1, wherein the dimension of the image is reduced or converted from the RGB color model to the HSV color model.

12. A device for simulating the trajectory of an object, comprising: At least one memory, on which multiple instructions are stored; as well as At least one processor is coupled to the memory, and when the instructions are executed, the processor is configured to: Extracting multiple frames from a video; In each of the images, multiple contour coordinates of the first tracked object are determined based on a first feature of the first tracked object; A first frame shape is identified based on the contour coordinates; A vector is generated based on the relative relationship between a reference coordinate and the coordinates of the center point of the first frame, and a trajectory of a target object is simulated based on the vector, wherein the target object is associated with the first tracking object; The trajectory of action is quantified and plotted on the corresponding coordinates of a data visualization plot; Determine the center point coordinates of a second tracking object, wherein the second tracking object has a second feature that is different from the first feature of the first tracking object; as well as The coordinates of the center point of the second tracked object are used as reference coordinates.

13. The apparatus of claim 12, wherein the target object is the same as the first tracking object or is directly or indirectly connected to the first tracking object.

14. The apparatus of claim 12, wherein the processor is configured to include setting a desired monitoring area range in the image capture so as to capture only the desired monitoring area range when capturing the video; and to include generating only the data visualization within the desired monitoring area range when drawing to the data visualization.

15. The apparatus of claim 14, wherein capturing the image further comprises dilating the area to be monitored to capture a rough area to be monitored.

16. The apparatus of claim 12, wherein determining the contour coordinates of the first tracked object in the image includes dilating and eroding the contour of the first tracked object.

17. The apparatus of claim 12, wherein the processor is configured to simulate the action trajectory by simulating the action trajectory based on an action trajectory parameter and the vector.

18. The apparatus of claim 12, wherein the processor is further configured to: Calculate the area of ​​a contour based on the contour coordinates; The contour area is compared with an actual area of ​​the first tracked object to produce a comparison result; and The contour coordinates that are outside the error range are excluded based on the comparison results.

19. The apparatus of claim 12, wherein the processor is further configured to: Calculate the area of ​​the first frame shape; Compare the area of ​​the first frame with an estimated area of ​​the first frame to produce a comparison result; and Based on the comparison result, the first frame shape that is outside the error range is excluded, and the contour coordinates that are outside the error range corresponding to the first frame shape are also excluded.

20. The apparatus of claim 12, wherein the processor is further configured to: Associate the action trajectory with at least one reference indicator; and This reference indicator is incorporated into the data visualization.

21. The apparatus of claim 12, wherein when the action trajectory is numerated, the processor is configured to set the same or different values ​​according to different position coordinates of the action trajectory.

22. The apparatus of claim 12, wherein the dimension of the image is reduced or converted from the RGB color model to the HSV color model.

Images