Target tracking method and system for virtual reality environment

Abstract

The invention relates to the technical field of target tracking, in particular to a target tracking method and system for a virtual reality environment, and the method comprises the steps: obtaining the angular velocity, acceleration and six-degree-of-freedom pose data of a tracking target at different moments in real time, and obtaining a posterior error covariance matrix at each moment in combination with a Kalman filtering algorithm; calculating the tracking misalignment degree of each moment; obtaining a shielding risk degree at each moment; and adjusting the process noise covariance matrix of the Kalman filtering algorithm, and estimating the pose of the tracking target by using the adjusted process noise covariance matrix in combination with the Kalman filtering algorithm. According to the invention, robust, continuous and high-precision tracking of the tracking target under the condition of shielding or sudden motion change can be realized, and it is ensured that pose estimation of the interaction object in the virtual reality environment is always stable and reliable.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of target tracking, in particular to a target tracking method and system for a virtual reality environment. BACKGROUND

[0002] In a virtual reality environment, target tracking technology is the key to realizing an immersive interactive experience. The spatial position information of the human body, eyeballs and interactive devices captured in real time forms a natural interactive channel, greatly improving the immersion and authenticity of the interactive experience in the virtual reality environment, and enabling major technical breakthroughs in gaming and social applications.

[0003] In a complex interactive scenario, the user's body turning, interactive operation or environmental obstacles often block the view of the tracking device, causing optical occlusion, resulting in loss or inaccuracy of the optical pose data of the tracking. When short-term pose estimation is performed on the data measured by an inertial measurement unit (IMU), the measured data has inherent noise and the integration process amplifies the error, resulting in significant drift in the pose estimation, which manifests as shaking, trailing or even complete loss of control of the virtual object, causing a decrease in the tracking accuracy of the target and severely damaging the user's immersion and interactive experience. SUMMARY

[0004] To solve the above technical problems, a target tracking method and system for a virtual reality environment are provided to solve the existing problems.

[0005] The technical problem of the present application is solved by providing a target tracking method and system for a virtual reality environment, comprising the following steps: In a first aspect, the embodiments of the present application provide a target tracking method for a virtual reality environment, comprising the following steps: Real-time acquisition of the angular velocity, acceleration and six-degree-of-freedom pose data of the tracking target at different times, and combination of a Kalman filtering algorithm to obtain the posterior error covariance matrix at each time; Based on the overall deviation of the posterior error covariance matrix and the deviation intensity in the dominant direction, the uncertainty and error of the pose estimation are evaluated, and the tracking misalignment degree at each time is calculated; The tracking misalignment degrees at different times are smoothed, the residual is calculated through the difference between the smoothed results at each time and the tracking misalignment degree, and the occlusion risk degree at each time is obtained in combination with the abnormal accumulation of the residual; Based on the occlusion risk degree, the process noise covariance matrix of the Kalman filtering algorithm is adjusted, and the pose of the tracking target is estimated using the adjusted process noise covariance matrix in combination with the Kalman filtering algorithm.

[0006] Preferably, the six-degree-of-freedom pose data is obtained by disposing an optical tracking device in an environment in which the tracking target is located, capturing an optical marker point on the tracking target by the optical tracking device, and calculating the six-degree-of-freedom pose data of the tracking target by a triangulation algorithm.

[0007] Preferably, the six-degree-of-freedom pose data comprises three-dimensional position coordinates and three-degree-of-freedom attitude data.

[0008] Preferably, the posterior error covariance matrix is obtained by taking the angular velocity, the acceleration at each time, and the six-degree-of-freedom pose data at each time corresponding to the previous time as inputs of a Kalman filtering algorithm, and obtaining the posterior error covariance matrix at each time.

[0009] Preferably, the tracking misalignment degree at each time is calculated by: performing eigenvalue decomposition on the posterior error covariance matrix to obtain all eigenvalues, and selecting the maximum eigenvalue; the tracking misalignment degree is the product of the trace of the posterior error covariance matrix at each time and the maximum eigenvalue.

[0010] Preferably, the residual is calculated by: taking each time and all times before the time as a local time period; smoothing the tracking misalignment degrees at all times in the local time period to obtain a smoothed value at each time; and taking the difference between the tracking misalignment degree and the smoothed value at each time as the residual at each time.

[0011] Preferably, the occlusion risk degree at each time is obtained by: performing anomaly detection on all residuals in the local time period at each time to obtain a cumulative offset at each time; the occlusion risk degree is the product of the residual and the cumulative offset at each time.

[0012] Preferably, the cumulative offset is obtained by: performing anomaly detection on all residuals in the local time period at each time by using a CUSUM algorithm, and outputting the cumulative offset at each time.

[0013] Preferably, the calculation formula of the adjusted process noise covariance matrix at each time is: wherein, is the normalized occlusion risk degree at each time, is the process noise covariance matrix before adjustment at each time.

[0014] ​​​Secondly, embodiments of this application also provide a target tracking system for a virtual reality environment, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the above-described target tracking methods for a virtual reality environment.

[0015] This application has at least the following beneficial effects: This application uses the Kalman filter algorithm to obtain the posterior error covariance matrix at each time step and calculates the tracking inaccuracy at each time step. Its advantage lies in quantifying the uncertainty of pose estimation through the posterior error covariance matrix, and further considering the overall uncertainty of the posterior error covariance matrix and the error amplification in the dominant direction, thus comprehensively reflecting the quality of target tracking. The tracking inaccuracy at different time steps is smoothed, and the residual is calculated based on the difference between the smoothing result and the tracking inaccuracy at each time step. Its advantage lies in considering the deviation of the tracking inaccuracy at that time step from the recent average level. The occlusion risk level is obtained at each time step, which is beneficial because it considers the continuous accumulation of tracking quality deviations and reflects the risk level of target occlusion at that time step. The process noise covariance matrix of the Kalman filter algorithm is adjusted. By utilizing the adjusted process noise covariance matrix and combining it with the Kalman filter algorithm, the pose of the tracked target is estimated. Its beneficial effect lies in dynamically adjusting the process noise covariance matrix of the Kalman filter algorithm based on the assessment of occlusion risk, thereby enhancing the flexibility and adaptability of the Kalman filter algorithm. This reduces the Kalman filter's reliance on IMU measurement data when occlusion occurs, making it more inclined to use predicted data to estimate the pose. This suppresses integral drift, reduces the accumulation of pose estimation errors, and maximizes the continuity and stability of tracking. This achieves robust, continuous, and high-precision tracking of the target under occlusion or sudden motion changes, ensuring that the pose estimation of interactive objects in virtual reality environments remains stable and reliable. Ultimately, this provides users with a continuous and accurate virtual interactive experience in complex interactive scenarios. Attached Figure Description

[0016] The target tracking method for virtual reality environments of this application will be further described in detail below with reference to the accompanying drawings.

[0017] Figure 1 A flowchart illustrating the steps of a target tracking method for a virtual reality environment provided in this application embodiment; Figure 2 A flowchart illustrating the steps of the method for obtaining the adjusted process noise covariance matrix provided in this application embodiment. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the target tracking method and system for virtual reality environments proposed in this application will be further described in detail below with reference to the accompanying drawings and implementation examples. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit the scope of this application.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0020] Please see Figure 1 The diagram illustrates a flowchart of a target tracking method for a virtual reality environment according to an embodiment of this application, the method comprising the following steps: Step 1: Real-time acquisition of the angular velocity, acceleration, and six-degree-of-freedom pose data of the tracked target at different times.

[0021] During target tracking, situations such as target occlusion are very likely to occur, and in more complex environments, it is difficult to obtain the target's posture information, affecting the accuracy and stability of tracking. This can cause virtual objects in the virtual environment to shake, drift, suddenly disappear, or lag, causing motion sickness in users and resulting in a poor human-computer interaction experience.

[0022] In this embodiment, the tracking targets are the user's head and the user's hand, therefore: Users wear head-mounted displays, which contain an inertial measurement unit (IMU) to collect the angular velocity and acceleration of the user's head in real time. Users wear an interactive controller, which has a built-in inertial measurement unit (IMU) to collect the angular velocity and acceleration of the user's hand in real time. Multiple optical tracking devices are deployed in the user's spatial environment to capture optical markers attached to the handle and the head-mounted display device. Using triangulation algorithms, the three-dimensional position coordinates of the tracking target and the three-degree-of-freedom attitude data, including pitch angle, roll angle and yaw angle, are calculated in real time to form six-degree-of-freedom pose data of the user's head and six-degree-of-freedom pose data of the handle. It should be noted that the format of the six-DOF pose data is as follows: ,in, These are the coordinates on the X-axis. In order to be in The coordinate values ​​of the axis. In order to be in The coordinate values ​​of the axis. Indicates pitch angle, Indicates the roll angle. Indicates the yaw angle.

[0023] In this embodiment, the data acquisition frequency is 10Hz. As for other implementation methods, the implementer can set it according to the actual situation.

[0024] The collected data is normalized to obtain the angular velocity, acceleration, and six-degree-of-freedom pose data of the user's head and hands at each moment during the user's movement. In this embodiment, the minimum-maximum normalization method is used for normalization processing. The minimum-maximum normalization method is a well-known technique and will not be described in detail here.

[0025] At this point, the angular velocity, acceleration, and six-degree-of-freedom pose data of the user's head and hands at each moment are obtained.

[0026] Step 2: Using the Kalman filter algorithm, obtain the posterior error covariance matrix at each time step; based on the overall deviation of the posterior error covariance matrix and the deviation intensity in the dominant direction, assess the uncertainty and error of pose estimation, and calculate the tracking inaccuracy at each time step.

[0027] In a virtual reality environment, if the target is temporarily out of the tracking range of the optical tracking device due to the user rotating their body, using the controller, or having their head obscured, the optical tracking device will be unable to provide visual positioning data and will only rely on the inertial measurement unit for attitude estimation. This will result in the accumulation of tracking errors, unsmooth movement of objects in the virtual environment, and even the object disappearing from the user's field of vision, thus damaging the user's immersive experience and the completion of interactive tasks.

[0028] The state equations and measurement equations of the Kalman filter system are as follows: ; ; in, for The state variable at time t, for The state variable at time t, State variables arrive The state transition matrix, for The control input matrix at each time step, for Time-based control input, for The measurement variable at time, for The measurement matrix at time, For process noise, To measure the noise, we assume that the noise particles are independent and follow a normal distribution. Then: , ; in, Let k be the process noise covariance matrix at time k. Let be the measurement noise covariance matrix at time k.

[0029] Therefore, the specific implementation steps of the Kalman filter algorithm are as follows: Based on the posterior state estimate from the previous time step First, predict the prior state estimate. and the prior error covariance matrix Specifically: State prediction: ; in, for Prior state estimation at time 10:00 for The state transition matrix at time t, for Posterior state estimation at time 10:00. for The control input matrix at each time step, for Time-based control input; Covariance prediction: ; in, for The prior error covariance matrix at time t. for The posterior error covariance matrix at time t. for The transpose of the matrix; State update: Calculate the residual, which is the error between the actual measured value and the prior state estimate, and correct the calculated prior state estimate using the minimum variance principle to obtain the posterior state estimate. Simultaneously, based on the prior error covariance matrix and the Kalman filter gain, the posterior error covariance matrix is ​​solved. .

[0030] ; ; in, for Posterior state estimation at time 10:00. for The measurement variable at time, Kalman gain; for The posterior error covariance matrix at time t. Represents the identity matrix; Kalman gain Calculation formula: ; in, for The transpose of the matrix; Therefore, the user's head is placed in angular velocity, acceleration and The six-DOF pose data at time 1 is used as input to the Kalman filter algorithm to obtain the user's head position. The posterior error covariance matrix at time t; Place the user's hand on angular velocity, acceleration and The six-DOF pose data at time 1 is used as input to the Kalman filter algorithm to obtain the user's hand position. The posterior error covariance matrix at time t; In this embodiment, the initial measurement noise covariance matrix is ​​set as follows: ,in, To represent a 6×6 identity matrix, The six-DOF pose data at time t is used as The posterior state estimation at time 1 will Angular velocity and acceleration at time t are used as The control input at any time is used to obtain... The posterior error covariance matrix at time t.

[0031] It should be noted that the Kalman filtering algorithm is a well-known technique and will not be elaborated upon here.

[0032] Furthermore, the posterior error covariance matrix quantifies the uncertainty of state estimation. Therefore, based on the output posterior error covariance matrix, the tracking inaccuracy is calculated as follows: Perform eigenvalue decomposition on the posterior error covariance matrix to obtain all eigenvalues, and select the largest eigenvalue; In this embodiment, the Eigenvalue decomposition (EVD) algorithm is used to perform feature decomposition and obtain feature values. The EVD algorithm is a well-known technology and will not be described in detail here. As other implementation methods, implementers may use other methods of existing technology, such as the SVD algorithm, etc. This embodiment does not impose any special restrictions on this.

[0033] Calculate the trace of the posterior error covariance matrix; It should be noted that the calculation process of the trace of a matrix is ​​a well-known technique and will not be elaborated here.

[0034] The product of the trace of the posterior error covariance matrix of the user's head at each time step and the largest eigenvalue is used as the tracking inaccuracy of the user's head at each time step. The product of the trace of the posterior error covariance matrix of the user's hand at each time step and the largest eigenvalue is used as the tracking inaccuracy of the user's hand at each time step. It should be noted that the trace of the posterior error covariance matrix reflects the overall uncertainty of the pose estimation at this time. The larger the value of the trace, the greater the overall uncertainty of the state estimation, the more divergent the pose error distribution of the Kalman filter estimation, and the lower the reliability of the target tracking state. The maximum eigenvalue reflects the error magnitude of the matrix in the direction of the eigenvector corresponding to the maximum eigenvalue. It increases with directional occlusion or sudden motion. The larger the maximum eigenvalue, the lower the reliability of the state estimation, which is mainly concentrated in the direction of the eigenvector corresponding to the maximum eigenvalue. It reflects the higher possibility of directional occlusion or sudden motion in this direction. The greater the obtained tracking inaccuracy, the worse the target tracking effect is at this time, and the greater the uncertainty and error of the pose estimation.

[0035] At this point, the tracking inaccuracies of the user's head and hands at various times are obtained.

[0036] Step 3: Smooth the tracking inaccuracy at different times. Calculate the residuals by comparing the smoothing results with the tracking inaccuracy at each time. Combine the abnormal accumulation of the residuals to obtain the occlusion risk at each time.

[0037] Furthermore, since optical occlusion in virtual reality environments is mostly sudden and brief, the tracking inaccuracy assessment evaluates the stability and accuracy of the Kalman filter at this time, but cannot determine the changes in tracking stability, nor can it predict whether the occlusion is just beginning or about to end, which will lead to response delays. As a result, it is impossible to grasp the best compensation time, and it is difficult to avoid pose estimation drift, resulting in a poor user interaction experience.

[0038] Based on the above analysis, the occlusion risk is calculated by analyzing the changes in tracking inaccuracy at different times to assess the deviation state of the Kalman filter. Specifically: Each moment and all moments preceding it are recorded as a local time interval; Smooth the tracking inaccuracy of the user's head at all times within a local time period to obtain the smoothed value of the user's head at each time point; In this embodiment, an exponentially weighted moving average algorithm is used for smoothing, wherein the smoothing factor is set to 0.1, so that the smoothing result pays more attention to recent changes in tracking quality. The exponentially weighted moving average algorithm is a well-known technology and will not be described in detail here.

[0039] It should be noted that the smoothing value reflects the short-term trend of target tracking quality and assesses the recent average level of target tracking quality.

[0040] The difference between the tracking inaccuracy of the user's head at each time step and the smoothing value is used as the residual; It should be noted that the residual reflects the degree of deviation of the current tracking inaccuracy from the recent average level. The larger the residual, the higher the deviation, indicating that the tracking quality has changed suddenly, possibly due to optical obstruction.

[0041] The CUSUM algorithm is used to detect anomalies in all residuals of the user's head within local time periods at each time point, and the cumulative offset of the user's head at each time point is obtained. It should be noted that the CUSUM algorithm is a well-known technology and will not be described in detail here. The threshold in the CUSUM algorithm is set to h=0.05. This value is set according to the tolerance for occlusion false alarm rate. As other implementation methods, implementers can set it according to the actual situation.

[0042] Calculate the product of the residual of the user's head at each time step and the cumulative offset, and use it as the occlusion risk of the user's head at each time step. To assess the tracking inaccuracy of the user's hand at various time points, the same calculation method used for the cumulative offset of the user's head at various time points is employed to calculate the occlusion risk of the user's hand at each time point. Specifically: Smooth the tracking inaccuracy of the user's hand at all times within a local time period to obtain the smoothing value of the user's hand at each time point; The difference between the tracking inaccuracy of the user's hand at each moment and the smoothing value is used as the residual; Anomaly detection is performed on all residuals of the user's hand within local time periods at each time point to obtain the cumulative offset of the user's hand at each time point; Calculate the product of the residual of the user's hand at each time step and the cumulative offset, and use it as the occlusion risk of the user's hand at each time step; It should be noted that the cumulative offset reflects the degree of cumulative abrupt change in tracking quality, indicating whether the Kalman filter has been continuously moving away from the normal state or whether there is a hidden drift or gradual degradation process. The larger the cumulative offset, the more the deviation in tracking quality is accumulating, and the Kalman filter is gradually moving away from the stable tracking state. The greater the occlusion risk, the higher the risk of the tracked target being occluded. In this case, the lower the reliability of the pose estimation, and there is a possibility that tracking failure is about to occur or has already occurred.

[0043] This gives us the risk of head and hand occlusion at various times.

[0044] Step 4: Based on the occlusion risk level, adjust the process noise covariance matrix of the Kalman filter algorithm. Using the adjusted process noise covariance matrix, combined with the Kalman filter algorithm, estimate the pose of the tracked target.

[0045] Furthermore, in virtual reality environments, the tracked target may temporarily disappear from the field of view of the optical tracking device due to the user turning around or being blocked by occlusions in the environment. At this time, the pose estimation is performed using only the angular velocity and acceleration collected by the inertial measurement unit, which has inherent noise and integral drift problems. This causes the pose estimation error to accumulate over time, resulting in distortion of the user's tracking trajectory and the virtual object shaking or disappearing.

[0046] Based on the above analysis, when the occlusion risk is high, the Kalman filter needs to be more flexible in adapting to sudden motion changes. By increasing the process noise covariance matrix of the Kalman filter algorithm, the degree of trust of the Kalman filter in IMU measurement data is reduced. In the case of missing visual positioning data, the Kalman filter is more inclined to use the prediction of the motion model to estimate the pose, thereby suppressing integral drift and reducing the accumulation of pose estimation error.

[0047] Therefore, based on the occlusion risk level, the process noise covariance matrix of the Kalman filter algorithm is adjusted as follows: ; ; in, For the user's head in The adjusted process noise covariance matrix at time step 1. For the user's head in The corresponding normalized occlusion risk level at any given moment. For the user's head in The process noise covariance matrix before adjustment at time step; For the user's hand in The adjusted process noise covariance matrix at time step 1. For the user's hand in The corresponding normalized occlusion risk level at any given moment. For the user's hand in The process noise covariance matrix before adjustment at time step; In this embodiment, the maximum-minimum normalization method is used for normalization processing. The maximum-minimum normalization method is a well-known technique and will not be described in detail here. As other implementation methods, implementers may use other methods of the prior art, such as the tanh function, etc. This embodiment does not impose any special restrictions on this.

[0048] It should be noted that a higher occlusion risk indicates the occurrence of occlusion or instability. Therefore, by increasing the process noise covariance matrix, the Kalman filter's trust in the motion model is reduced, allowing the Kalman filter to use predicted IMU data instead of IMU measurement data containing significant noise to estimate the pose. After the occlusion ends, the occlusion risk decreases, and the process noise covariance matrix gradually recovers to its original size, smoothly returning to normal tracking. The flowchart of the method for obtaining the adjusted process noise covariance matrix provided in this embodiment is shown below. Figure 2 As shown.

[0049] Based on the adjusted process noise covariance matrix and combined with the Kalman filter algorithm, the six degrees of freedom poses of the user's head and hands are estimated respectively, so as to achieve robust, continuous and high-precision tracking of the tracked target under occlusion or sudden motion, ensuring that the pose estimation of interactive objects in the virtual reality environment is always stable and reliable, thus providing users with a continuous and accurate virtual interactive experience in relatively complex interactive scenarios.

[0050] Based on the same inventive concept as the above methods, embodiments of this application also provide a target tracking system for a virtual reality environment, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any one of the above-described target tracking methods for a virtual reality environment.

[0051] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0052] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0053] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of this application, without departing from the content of the technical solution of this application, shall fall within the protection scope of the technical solution of this application.

Claims

1. A target tracking method for a virtual reality environment, characterized by, The method comprises the following steps: Real-time acquisition of angular velocity, acceleration and six-degree-of-freedom pose data of the tracking target at different time instants, and acquisition of posterior error covariance matrix at each time instant by combining Kalman filtering algorithm; Based on the overall deviation of the posterior error covariance matrix and the deviation intensity in the dominant direction, the uncertainty and error of the pose estimation are evaluated, and the tracking misalignment degree at each time instant is calculated; The tracking misalignment degrees at different time instants are smoothed, the residual is calculated through the difference between the smoothing result at each time instant and the tracking misalignment degree, and the occlusion risk degree at each time instant is obtained by combining the abnormal accumulation of the residual; Based on the occlusion risk degree, the process noise covariance matrix of the Kalman filtering algorithm is adjusted, and the pose of the tracking target is estimated by combining the adjusted process noise covariance matrix and the Kalman filtering algorithm.

2. The target tracking method for a virtual reality environment of claim 1, wherein, The acquisition process of the six-degree-of-freedom pose data is: optical tracking equipment is arranged in the environment where the tracking target is located, the optical tracking equipment captures the optical marker point on the tracking target, and the six-degree-of-freedom pose data of the tracking target is calculated by using the triangulation algorithm.

3. The target tracking method for a virtual reality environment of claim 1, wherein, The six-degree-of-freedom pose data includes three-dimensional position coordinates and three-degree-of-freedom attitude data.

4. The target tracking method for a virtual reality environment of claim 1, wherein, The acquisition method of the posterior error covariance matrix is: the angular velocity, acceleration and six-degree-of-freedom pose data of the previous time instant corresponding to each time instant are taken as the input of the Kalman filtering algorithm, and the posterior error covariance matrix at each time instant is obtained.

5. The target tracking method for a virtual reality environment of claim 1, wherein, The calculation of the tracking misalignment degree at each time instant comprises: The posterior error covariance matrix is subjected to eigenvalue decomposition, all eigenvalues are obtained, and the maximum eigenvalue is selected; The tracking misalignment degree is the product of the trace of the posterior error covariance matrix at each time instant and the maximum eigenvalue.

6. The target tracking method for a virtual reality environment of claim 1, wherein, The calculation process of the residual is: all time instants before each time instant are recorded as a local period; the tracking misalignment degrees of all time instants in the local period are smoothed to obtain the smoothing value at each time instant; and the difference between the tracking misalignment degree and the smoothing value at each time instant is taken as the residual at each time instant.

7. The target tracking method for a virtual reality environment of claim 6, wherein, The occlusion risk degree at each time instant is obtained by: Abnormal detection is performed on all residuals in the local period at each time instant to obtain the cumulative offset at each time instant; The occlusion risk degree is the product of the residual and the cumulative offset at each time instant.

8. The target tracking method for a virtual reality environment of claim 7, wherein, The acquisition process of the cumulative offset is: CUSUM algorithm is used to perform abnormal detection on all residuals in the local period at each time instant, and the cumulative offset at each time instant is output.

9. The target tracking method for a virtual reality environment of claim 1, wherein, adjusted process noise covariance matrix at time k The calculation formula is: wherein, is normalized occlusion risk degree corresponding to time k, is adjusted process noise covariance matrix at time k.

10. A target tracking system for a virtual reality environment, comprising a memory, a processor and a computer program stored in the memory and running on the processor, characterized in that, The processor executes the computer program to realize the steps of the target tracking method for virtual reality environment according to any one of claims 1-9.

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