Method for managing tracking devices, device manager, and computer readable storage medium

Abstract

The embodiments of the disclosure provide a method for managing tracking devices, a device manager, and a computer readable storage medium. The method includes: receiving a local map corresponding to each of a plurality of tracking devices and accordingly generating a shared map; obtaining a pose ambiguity status corresponding to each of the tracking devices; determining at least one first specific device among the tracking devices based on the pose ambiguity status corresponding to each of the tracking devices; and disabling at least one function of the at least one first specific device.

Description

[Technical Field]

[0001] This invention relates to a tracking mechanism, and more particularly to a method for managing tracking devices, a device manager, and a computer-readable storage medium. [Previous Technology]

[0002] Referring to Figure 1A, Figure 1A illustrates a scenario where an outside-in tracking is performed using a tracking device. In Figure 1A, the tracking device 101 can be used to capture an image of an object 199 (e.g., a human body) and perform outside-in tracking based on the image to obtain the pose of the object 199 (i.e., the pose of the human body). However, in some cases where part of the object 199 (e.g., the lower half of the human body) is occluded by an obstacle 110, the tracking device 101 in Figure 1A may not achieve satisfactory pose tracking performance.

[0003] Referring to Figure 1B, Figure 1B illustrates a scenario where multiple tracking devices are used to perform outside-in tracking. In Figure 1B, multiple tracking devices 101 to 103 can be deployed to solve the problem mentioned in Figure 1A. In this scenario, it is crucial to determine which of the tracking devices 101 to 103 can be used to track the object 199. [Summary of the Invention]

[0004] In view of the above, the present invention provides a method for managing a tracking device, a device manager, and a computer-readable storage medium, which can be used to solve the above-mentioned technical problems.

[0005] Embodiments of the present invention provide a method for managing tracking devices. The method includes: receiving a local map corresponding to each of a plurality of tracking devices by a device manager and generating a shared map thereon; obtaining an attitude ambiguity state corresponding to each of the tracking devices by the device manager; determining at least one first specific device among the plurality of tracking devices based on the attitude ambiguity state corresponding to each of the tracking devices by the device manager; and disabling at least one function of the at least one first specific device by the device manager.

[0006] Embodiments of the present invention provide a device manager, the device manager including a storage circuit and a processor. The storage circuit stores program code. The processor is coupled to the storage circuit and accesses the program code to perform: receiving a local map corresponding to each of a plurality of tracking devices and generating a shared map thereon; obtaining an attitude ambiguity state corresponding to each of the tracking devices; determining at least one first specific device among the plurality of tracking devices based on the attitude ambiguity state corresponding to each of the tracking devices; and disabling at least one function of the at least one first specific device.

[0007] This embodiment of the invention provides a non-transitory computer-readable storage medium, wherein the computer-readable storage medium records an executable computer program, which is loaded by a device manager to perform the following steps: receiving a local map corresponding to each of a plurality of tracking devices and generating a shared map thereon; obtaining an attitude ambiguity state corresponding to each of the tracking devices; determining at least one first specific device among the plurality of tracking devices based on the attitude ambiguity state corresponding to each of the tracking devices; and disabling at least one function of the at least one first specific device.

Implementation Method

[0009] Referring to FIG2, FIG2 shows a functional diagram of a device manager according to an embodiment of the present invention. In FIG2, the device manager 200 includes a storage circuit 202 and a processor 204. The storage circuit 202 is one or a combination of fixed or removable random access memory (RAM), read-only memory (ROM), flash memory, hard disk, or any other similar device, and the storage circuit 202 records program code and / or multiple modules executable by the processor 204.

[0010] The processor 204 is coupled to the storage circuit 202, and the processor 204 may be, for example, a general-purpose processor, a special-purpose processor, a conventional processor, a digital signal processor (DSP), multiple microprocessors, one or more microprocessors associated with a DSP core, a controller, a microcontroller, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) circuit, any other type of integrated circuit (IC), a state machine, etc.

[0011] In various embodiments, the device manager 200 may be used to manage one or more electronic devices that collectively perform an outside-in tracking mechanism. In one embodiment, the electronic devices include, but are not limited to, one or more tracking devices and / or head-mounted displays (HMDs).

[0012] In one embodiment, the tracking device may be equipped with a camera and an image processor. In one embodiment, the camera on the tracking device may be used to perform an image capturing function for capturing images of the object to be tracked. Additionally, the image processor on the tracking device may be used to perform a tracking function for tracking the posture of the object. For example, when the object to be tracked is a human body, the image processor may be used to detect joints on the human body in the images captured by the camera and determine the posture of the human body accordingly, but the invention is not limited thereto. In other embodiments, the object to be tracked may be other types of moving objects, such as various animals or mobile devices.

[0013] In one embodiment, the tracking device may be positioned in a specific field of view (e.g., a room or other specific space) for the joint tracking of objects moving within the specific field of view.

[0014] Referring to FIG3, FIG3 illustrates a scenario of tracking objects in a specific field of view using a tracking device according to an embodiment of the present invention. In FIG3, tracking devices 301 to 303 can be deployed in a specific field of view 300 to track the posture of objects 399a and 399b to be tracked. In an embodiment, objects 399a and 399b to be tracked can be users wearing HMD 310 and HMD 312, whereby users can simultaneously immerse themselves in the same virtual environment (e.g., a virtual reality (VR) world) by viewing content provided by the HMD. In one embodiment, the tracking results obtained by tracking devices 301 to 303 can be provided to HMD 310 and HMD 312, for example, so that HMD 310 and HMD 312 can adjust the content displayed to the user accordingly, but the present invention is not limited thereto. In one embodiment, HMD 310 and HMD 312 may be standalone HMDs capable of generating content for users to view, but the present invention is not limited thereto.

[0015] In some embodiments, the device manager 200 may be used to manage tracking device 301 to tracking device 302 by, for example, disabling / enabling certain functions of the tracking device.

[0016] In one embodiment, HMD 310 and HMD 312 may be considered as part of a tracking device managed by device manager 200. In one embodiment, device manager 200 is implemented as one of HMD 310, HMD 312, and tracking devices 301 to 303. In one embodiment, device manager 200 may be implemented as another device (e.g., a computer and / or a smart device) independent of the devices shown in FIG3, but the invention is not limited thereto.

[0017] In one embodiment, the tracking results obtained by the tracking device may be provided to other electronic devices for further processing / presentation. For example, the tracking results (e.g., a skeleton formed by connecting joints on the human body) may be provided to a specific display for display, but the invention is not limited thereto.

[0018] In an embodiment of the present invention, the processor 204 accesses modules and / or code stored in the storage circuit 202 to implement the method for managing a tracking device provided in the present invention, which will be further discussed below.

[0019] Referring to FIG4, FIG4 shows a flowchart of a method for managing a tracking device according to an embodiment of the present invention. The method of this embodiment can be executed by the device manager 200 in FIG2, and the details of each step in FIG4 will be explained below using the elements shown in FIG2. In addition, the scenario in FIG3 will be used as an example to better illustrate the concepts of FIG4.

[0020] In step S410, the processor 204 receives the local map corresponding to each of the tracking devices 301 to 303 and generates a shared map accordingly. In one embodiment, each of the tracking devices 301 to 303 may perform simultaneous localization and mapping (SLAM) to construct its own local map (e.g., a SLAM map). For example, the tracking device 301 may perform SLAM based on captured images of its environment. Thus, the tracking device 301 can obtain feature points in the environment as its local map, but the invention is not limited thereto. Similarly, other tracking devices may perform SLAM to obtain feature points in the environment as the local map of the corresponding tracking device.

[0021] In one embodiment, HMD 310 and HMD 312 may also perform SLAM to obtain feature points in the environment as local maps of the corresponding HMD.

[0022] In one embodiment, each of tracking devices 301 to 303 (and HMDs 310 and 312) provides its own local map to device manager 200. In one embodiment, each of tracking devices 301 to 303 (and HMDs 310 and 312) may provide its own local map to one of the tracking devices 301 to 303 and HMDs 310 and 312, and this device may forward the local maps of tracking devices 301 to 303 and HMDs 310 and 312 to device manager 200, but the invention is not limited thereto.

[0023] In one embodiment, after receiving the local maps corresponding to tracking devices 301 to 303 (and HMDs 310 and 312), the processor 204 combines the local maps to generate a shared map. For example, the processor 204 may obtain multiple first feature points in a first local map corresponding to tracking device 301 and multiple second feature points in a second local map corresponding to tracking device 302. Next, the processor 204 determines multiple shared feature points between the first feature points and the second feature points. In one embodiment, the processor 204 considers feature points that belong to both the first and second feature points as shared feature points. Thus, the processor 204 constructs a shared map based on the shared feature points. For a detailed discussion of constructing a shared map, please refer to "Lajoie, Pierre-Yves, et al. "Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape." (2021). It will not be elaborated upon here.

[0024] In other embodiments, the processor 204 may identify feature points that simultaneously belong to some or all of the received local maps as common feature points and construct a common map accordingly, but the present invention is not limited thereto.

[0025] In one embodiment, after constructing the shared map, the processor 204 provides the shared map to the tracking devices 301 to 303 (and HMD 310, HMD 312) so that the tracking devices 301 to 303 (and HMD 310, HMD 312) know the device location of each of the tracking devices 301 to 303 (and HMD 310, HMD 312) in the shared map.

[0026] In step S420, the processor 204 obtains the attitude ambiguity state corresponding to the tracking devices 301 to 303. In one embodiment, the attitude ambiguity state corresponding to a tracking device includes the ambiguity of the tracking device regarding the attitude of at least one object (e.g., object 399a, object 399b).

[0027] For better understanding, tracking device 301 will be used as an example, and based on the following teachings, those skilled in the art should be able to understand the operations performed by other tracking devices.

[0028] In the first embodiment, the pose ambiguity state corresponding to the tracking device 301 is characterized by the tracking confidence of the tracking device 301 with respect to objects 399a and 399b. In one embodiment, when the image processor on the tracking device 301 performs a tracking function to track the pose of objects 399a and 399b, the image processor can provide a tracking confidence related to the performance of tracking each of objects 399a and 399b. For example, if the image processor is confident in its performance in tracking the objects, the relevant tracking confidence will be high. On the other hand, if the image processor is not confident in its performance in tracking the objects, the relevant tracking confidence will be relatively low, but the invention is not limited thereto.

[0029] In the second embodiment, the pose ambiguity state corresponding to the tracking device 301 can be characterized by the portion of each of the objects 399a and 399b captured in the image captured by the camera of the tracking device 301. Generally, the more of the main body of the object to be tracked is captured in the image, the better the tracking accuracy is likely to be. Taking object 399a as an example, if the captured portion of object 399a is close to the whole of object 399a (i.e., object 399a is less occluded), it indicates that the tracking device 301 is more likely to accurately track the pose of object 399a. Conversely, if the captured portion of object 399a is only a small part of the whole of object 399a (e.g., object 399a may be occluded), it indicates that the tracking device 301 is less likely to accurately track the pose of object 399a.

[0030] In the third embodiment, the pose ambiguity state corresponding to the tracking device 301 can be characterized as the number of joints of each of the objects 399a and 399b identified in the image. Generally, the more joints of the object to be tracked are identified in the image, the better the tracking accuracy is likely to be. Taking object 399a as an example, if the number of identified joints of object 399a is close to a predetermined number (e.g., 25), it indicates that the tracking device 301 is likely to accurately track the pose of object 399a. Conversely, if the number of identified joints of object 399a is only a few (e.g., 5), it indicates that the tracking device 301 is less likely to accurately track the pose of object 399a.

[0031] In the fourth embodiment, the attitude ambiguity state corresponding to the tracking device 301 can be characterized as the ratio of object 399a and object 399b relative to the image. Generally speaking, the larger the overall ratio of the object to be tracked relative to the image, the better the tracking accuracy is likely to be. Taking object 399a as an example, if the overall ratio of object 399a relative to the image is high, it means that the tracking device 301 is likely to accurately track the attitude of object 399a. Conversely, if the overall ratio of object 399a relative to the image is low, it means that the tracking device 301 is less likely to accurately track the attitude of object 399a.

[0032] In other embodiments, the attitude ambiguity state can be determined based on a combination of teaching content from two or more embodiments in the first to fourth embodiments, but the present invention is not limited thereto.

[0033] In one embodiment, each of the tracking devices 301 to 303 may provide its own pose ambiguity state to the device manager 200. In another embodiment, each of the tracking devices 301 to 303 may provide its own pose ambiguity state to one of the tracking devices 301 to 303 (and HMD 310, HMD 312), and this device may forward the pose ambiguity of the tracking devices 301 to 303 to the device manager 200, but the invention is not limited thereto.

[0034] In one embodiment, the processor 204 obtains the attitude ambiguity state corresponding to each of the tracking devices 301 to 303 based on a shared map. For example, after receiving the attitude ambiguity state determined by the tracking device 301, the processor 204 may determine the attitude ambiguity state of, for example, the tracking device 302 based on the shared map.

[0035] In one embodiment, the processor 204 may determine the relative position between the device position of the tracking device 301 and the device position of the tracking device 302. Next, the processor 204 may estimate the pose ambiguity state corresponding to the tracking device 302 based on the relative position between the device positions of the tracking device 301 and the tracking device 302. In this embodiment of the invention, the relative position between the device positions of the tracking device 301 and the tracking device 302 may be characterized by the distance between them and / or their viewing angle (i.e., the direction of the captured image).

[0036] In the exemplary scenario of Figure 3, it is assumed that tracking device 301 and tracking device 302 are taking pictures in opposite directions, with tracking device 301 shooting to the right and tracking device 302 shooting to the left. In this case, assuming objects 399a and 399b are facing and moving toward tracking device 301, processor 204 can estimate (based on the relative position between the device positions of tracking device 301 and tracking device 302) that objects 399a and 399b are moving away from tracking device 302, and tracking device 302 can only capture the back side of objects 399a and 399b. In this case, the pose ambiguity state (e.g., tracking confidence) of tracking device 302 will be estimated as low.

[0037] In addition, the processor 204 may also estimate the attitude ambiguity state corresponding to the tracking device 303 based on the relative position between the device position of the tracking device 301 and the device position of the tracking device 303.

[0038] In the exemplary scenario of Figure 3, it is also assumed that the tracking device 303 is shooting to the right. In this case, assuming that objects 399a and 399b are facing the tracking device 301, the processor 204 can estimate (based on the relative position between the device position of the tracking device 301 and the device position of the tracking device 303) that the tracking device 303 is almost unable to capture objects 399a and 399b. In this case, the pose ambiguity state (e.g., tracking confidence) of the tracking device 303 will be estimated to be lower.

[0039] In step S430, the processor 204 determines at least one first specific device among the tracking devices 301 to 303 based on the pose ambiguity state corresponding to each of the tracking devices 301 to 303. Next, in step S440, the processor 204 disables the function of the at least one first specific device. In one embodiment, the processor 204 disables the tracking function while maintaining the image capturing function of the at least one first specific device. That is, each first specific device will continue to capture images but will not track the pose of the objects captured in the images. In some embodiments, the processor 204 may also disable other functions of the at least one first specific device by, for example, disconnecting the at least one first specific device, switching the at least one first specific device to a power-saving mode, reducing the image capture playback rate, disabling the image capturing function, and / or stopping the transmission of local / shared maps, but the present invention is not limited thereto.

[0040] In one embodiment, the processor 204 determines at least one second specific device among the tracking devices 301 to 303 based on the pose ambiguity state corresponding to each of the tracking devices 301 to 303. The processor 204 may then enable or maintain the tracking function of the at least one second specific device.

[0041] In the first embodiment, the processor 204 determines whether the tracking confidence of the first tracking device (e.g., one of tracking devices 301 to 303) with respect to each of objects 399a and 399b is lower than a confidence threshold. In response to determining that the tracking confidence of the first tracking device with respect to each of objects 399a and 399b is lower than the confidence threshold, the processor 204 can determine that the first tracking device belongs to the at least one first specific device. That is, if the first tracking device lacks confidence in the tracking performance of the captured object, the first tracking device will be considered as one of the first specific devices whose tracking function will be disabled.

[0042] Conversely, in response to determining that the tracking confidence of the first tracking device with respect to one of objects 399a and 399b is not lower than a confidence threshold, the processor 204 may determine that the first tracking device belongs to the at least one second specific device. That is, if the first tracking device is confident in its tracking performance for at least one of the captured objects, the first tracking device will be considered as one of the second specific devices for which the tracking function will be enabled / maintained.

[0043] In the second embodiment, the processor 204 determines whether the captured portion of each of objects 399a and 399b in the image is less than a predetermined percentage of the total of each of objects 399a and 399b. In response to the determination that the captured portion of each of objects 399a and 399b in the image is less than a predetermined percentage of the total of each of objects 399a and 399b, the processor 204 may determine that the first tracking device belongs to the at least one first specific device. That is, if the captured portion of each of objects 399a and 399b is too small, the first tracking device will be considered as one of the first specific devices whose tracking function will be disabled.

[0044] Conversely, in response to the determination that the captured portion of one of objects 399a and 399b in the image is not less than a predetermined percentage of the total of each of objects 399a and 399b, the processor 204 may determine that the first tracking device belongs to the at least one second specific device. That is, if the captured portion of one of objects 399a and 399b is large enough, the first tracking device will be considered as one of the second specific devices to which the tracking function will be enabled / maintained.

[0045] In the third embodiment, the processor 204 determines whether the number of joints of each of the objects 399a and 399b identified in the image is less than a quantity threshold. In response to the determination that the number of joints of each of the objects 399a and 399b identified in the image is less than the quantity threshold, the processor 204 can determine that the first tracking device belongs to the at least one first specific device. That is, if the number of identified joints of each of the objects 399a and 399b is too small, the first tracking device will be considered as one of the first specific devices whose tracking function will be disabled.

[0046] Conversely, if the number of joints of one of the objects 399a and 399b identified in the determination image is not less than a quantity threshold, the processor 204 can determine that the first tracking device belongs to at least one second specific device. That is, if the number of identified joints of at least one of the objects 399a and 399b is sufficiently high, the first tracking device will be considered as one of the second specific devices for which the tracking function will be enabled / maintained.

[0047] In the fourth embodiment, the processor 204 determines whether the overall proportion of each of objects 399a and 399b relative to the image is less than a proportion threshold. In response to determining that the overall proportion of each of objects 399a and 399b relative to the image is less than the proportion threshold, the processor 204 can determine that the first tracking device belongs to the at least one first specific device. That is, if the overall size of each of objects 399a and 399b is too small in the image captured by the first tracking device, the first tracking device will be considered one of the first specific devices whose tracking function will be disabled.

[0048] Conversely, in response to the determination that the overall proportion of each of objects 399a and 399b relative to the image is not less than a proportion threshold, the processor 204 may determine that the first tracking device belongs to at least one second specific device. That is, if the overall proportion of at least one of objects 399a and 399b is not too small in the image captured by the first tracking device, then the first tracking device will be considered as one of the second specific devices for which the tracking function will be enabled / maintained.

[0049] In one embodiment, in response to determining that the computational workload of the at least one second specific device is greater than a workload threshold, the processor 204 may enable the tracking function of at least one of the at least one first specific device. That is, when at least one of the second specific devices whose tracking function is maintained / enabled is too busy, some of the first specific devices whose tracking function is disabled may be enabled to reduce the computational workload, but the invention is not limited thereto.

[0050] In some embodiments, in response to determining that the pose ambiguity state of a second tracking device with respect to a specific object is sufficiently high, the processor 204 may assign the second tracking device to track the specific object.

[0051] In the exemplary scenario of FIG3, it is assumed that tracking device 301 and tracking device 302 take pictures in opposite directions, wherein tracking device 301 takes pictures to the right and tracking device 302 takes pictures to the left. In this case, it is assumed that object 399a faces tracking device 301 and moves toward tracking device 301, while object 399b faces tracking device 302 and moves toward tracking device 302. Since the tracking confidence of tracking device 301 with respect to object 399a is high (or the captured portion of object 399a by tracking device 301 is large, the number of identified joints on object 399a is large, or the overall proportion of object 399a relative to the image captured by tracking device 301 is high), processor 204 can assign tracking device 301 to track object 399a. Similarly, since the tracking device 302 has high tracking reliability for the object 399b (or the captured portion of the object 399b by the tracking device 302 is large, the number of identified joints on the object 399b is large, or the overall proportion of the object 399b to the image captured by the tracking device 302 is high), the processor 204 can assign the tracking device 302 to track the object 399b, but the present invention is not limited thereto.

[0052] The present invention also provides a computer-readable storage medium for performing a method for managing a tracking device. The computer-readable storage medium comprises a plurality of program instructions contained therein (e.g., setup instructions and deployment instructions). These program instructions can be loaded into and executed by a device manager 200 to perform the aforementioned method for managing a tracking device and the functions of the device manager 200.

[0053] In summary, embodiments of the present invention provide a mechanism for determining a first specific device from the tracking device based on the attitude ambiguity state of the tracking device and thereby disabling the tracking function of the first specific device. Since the first specific device can be understood as a device with poor tracking performance, the computing resources of the first specific device can be saved after the tracking function is disabled. In addition, for those second specific devices whose tracking function is enabled / maintained, the tracking accuracy and efficiency can be improved. Thus, the overall tracking performance of the tracking device can be improved while saving some computing resources.

[0054] Although the present invention has been disclosed above by way of embodiments, it is not intended to limit the present invention. Anyone skilled in the art can make some modifications and refinements without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims. [Simplified Explanation of the Diagram]

[0008] Figure 1A illustrates a scenario where outside-to-in tracking is performed using a single tracking device. Figure 1B illustrates a scenario where outside-to-in tracking is performed using multiple tracking devices. Figure 2 shows a functional diagram of a device manager according to an embodiment of the present invention. Figure 3 illustrates a scenario where objects are tracked within a specific field of view using tracking devices according to an embodiment of the present invention. Figure 4 shows a flowchart of a method for managing tracking devices according to an embodiment of the present invention.

Claims

1. A method for managing a tracking device, comprising: The device manager receives the local map corresponding to each of the multiple tracking devices and generates a shared map accordingly. The device manager obtains the attitude ambiguity state corresponding to each of the tracking devices; the device manager determines at least one first specific device among the tracking devices based on the attitude ambiguity state corresponding to each of the tracking devices; and the device manager disables at least one function of the at least one first specific device.

2. The method as described in claim 1, wherein the tracking devices include a first tracking device and a second tracking device, and the step of generating the shared map includes: The device manager obtains multiple first feature points in a first local map corresponding to the first tracking device; The device manager obtains multiple second feature points in a second local map corresponding to the second tracking device; the device manager determines multiple shared feature points between the first feature points and the second feature points; and the device manager constructs the shared map based on the shared feature points.

3. The method as described in claim 1, further comprising: The device manager provides the shared map to the tracking devices, wherein the shared map includes the device location of each tracking device.

4. The method of claim 1, wherein the tracking devices include a first tracking device, the attitude ambiguity state corresponding to the first tracking device is characterized as the tracking confidence of the first tracking device with respect to at least one object, and the method includes: In response to determining that the tracking reliability of the first tracking device with respect to each of the at least one object is lower than a reliability threshold, the device manager determines that the first tracking device belongs to the at least one first specific device; in response to determining that the tracking reliability of the first tracking device with respect to one of the at least one object is not lower than the reliability threshold, the device manager determines that the first tracking device belongs to at least one second specific device.

5. The method of claim 1, wherein the tracking devices include a first tracking device for capturing an image of at least one object, the pose ambiguity state corresponding to the first tracking device being characterized as the captured portion of each of the at least one object in the image, and the method comprising: The device manager determines that the first tracking device belongs to the at least one first specific device based on the determination that the captured portion of each of the at least one objects in the image is less than a predetermined percentage of the total of each of the at least one objects. And in response to determining that the captured portion of one of the at least one objects in the image is not less than a predetermined percentage of the total of each of the at least one objects, the device manager determines that the first tracking device belongs to at least one second specific device.

6. The method of claim 1, wherein the tracking devices include a first tracking device for capturing an image of at least one object, the pose ambiguity state corresponding to the first tracking device being characterized by the number of joints of each of the at least one object identified in the image, and the method comprising: In response to determining that the number of joints of each of the at least one object identified in the image is less than a quantity threshold, the device manager determines that the first tracking device belongs to the at least one first specific device; and in response to determining that the number of joints of one of the at least one object identified in the image is not less than the quantity threshold, the device manager determines that the first tracking device belongs to at least one second specific device.

7. The method of claim 1, wherein the tracking devices include a first tracking device for capturing an image of at least one object, the pose ambiguity state corresponding to the first tracking device being characterized by the proportion of the at least one object as a whole relative to the image, and the method comprising: In response to determining that the proportion of the entirety of each of the at least one object relative to the image is less than a proportion threshold, the device manager determines that the first tracking device belongs to the at least one first specific device; and in response to determining that the proportion of the entirety of each of the at least one object relative to the image is not less than the proportion threshold, the device manager determines that the first tracking device belongs to at least one second specific device.

8. The method of claim 1, wherein the tracking devices include a first tracking device, and the step of receiving the local map corresponding to each of the tracking devices includes: The device manager receives the local map corresponding to each tracking device, which is forwarded by the first tracking device.

9. The method of claim 1, wherein the tracking devices include a first tracking device, and the step of obtaining the attitude ambiguity state corresponding to each of the tracking devices includes: The device manager receives the attitude ambiguity status, which is determined by each of the tracking devices and forwarded by the first tracking device.

10. The method of claim 1, wherein the tracking devices include a first tracking device and a second tracking device, and the step of obtaining the attitude ambiguity state corresponding to each of the tracking devices includes: The attitude ambiguity state corresponding to each tracking device is obtained based on the shared map.

11. The method of claim 10, wherein the common map includes the device locations of each of the tracking devices, and the step of obtaining the attitude ambiguity state corresponding to each of the tracking devices based on the common map includes: The device manager receives the attitude ambiguity state determined by the first tracking device; Determine the relative position between the device position of the first tracking device and the device position of the second tracking device; estimate the attitude ambiguity state corresponding to the second tracking device based on the relative position between the device positions of the first tracking device and the second tracking device.

12. The method of claim 1, wherein the tracking devices include a first tracking device, and the attitude ambiguity state corresponding to the first tracking device includes ambiguity of the first tracking device with respect to tracking the attitude of at least one object.

13. The method of claim 1, wherein the tracking devices include at least one head-mounted display.

14. The method as described in claim 1, wherein the device manager is one of the tracking devices.

15. The method as described in claim 1, further comprising: The device manager determines at least one second specific device among the tracking devices based on the attitude ambiguity state corresponding to each tracking device; And the tracking function of the at least one second specific device enabled or maintained by the device manager.

16. The method as described in claim 15, further comprising: In response to determining that the computational workload of the at least one second specific device is higher than the workload threshold, the tracking function of at least one of the at least one first specific device is enabled.

17. The method of claim 1, wherein each of the tracking devices has an image capturing function and a tracking function, and the step of disabling the tracking function of the at least one first specific device includes: The tracking function is disabled while maintaining the image capturing function of at least one first specific device.

18. The method of claim 17, wherein the step of disabling the tracking function while maintaining the image acquisition function of the at least one first specific device includes: Control each of the at least one first specific device to capture an image but do not track the pose of at least one object captured in the image.

19. A device manager, comprising: Storage circuit, stored program code; A processor, coupled to the storage circuit and accessing the code, executes: receiving a local map corresponding to each of the plurality of tracking devices and generating a shared map thereon; obtaining an attitude ambiguity state corresponding to each of the tracking devices; determining at least one first specific device among the tracking devices based on the attitude ambiguity state corresponding to each of the tracking devices; and disabling at least one function of the at least one first specific device.

20. A computer-readable storage medium recording an executable computer program loaded by a device manager to perform the following steps: receiving a local map corresponding to each of a plurality of tracking devices and generating a shared map thereon; obtaining an attitude ambiguity state corresponding to each of the tracking devices; determining at least one first specific device among the tracking devices based on the attitude ambiguity state corresponding to each of the tracking devices; and disabling at least one function of the at least one first specific device.

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