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US 2020/0025909 A1Published application
Multi-target tracking method and tracking system applicable to clutter environment
AssigneeShenzhen University
InventorsZONGXIANG LIU, LIANGQUN LI, WEIXIN XIE
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Abstract
A multi-target tracking method applicable to a cluttered environment includes a prediction step, a classification step, an updating step, a pruning and extracting step, a generation step, a supplement step and a combining step. A multi-target tracking system applicable to a cluttered environment is also provided. The present invention has the characteristic of a fast processing speed, and at the same time, effectively solves the problem that the existing method fails to provide state estimation for a new target in the initial few moments after the new target appears.
Claims13
A multi-target tracking method applicable to a clutter environment, comprising:
- a prediction step, using marginal distribution and existence probability of each target at the previous moment, and time interval between the current moment and the previous moment to predict marginal distribution and existence probability of each existing target at the current moment;
- wherein k−1 represents the previous moment, k represents the current moment, tk−1 represents the time of the previous moment, tk represents the time of the current moment, and the marginal distribution and existence probability of the target i at moment k−1 are represented as N(xi,k−1;
- mi,k−1, Pi,k−1) and ρi,k−1 respectively, wherein N represents a Gaussian distribution, i=1,2, . . . Nk−1, xi,k−1 is the state vector of the target i at moment k−1, mi,k−1 and Pi,k−1 represent the state mean and covariance of the target i at moment k−1, respectively, and Nk−1 is the total number of the targets at the previous moment;
- based on the marginal distribution N(xi,k−1;
- mi,k−1, Pi,k−1) and the existence probability ρi,k|k−1 of target i at moment k−1, the predicted marginal distribution and existence probability of existing target i at moment k are given by N(xi,k;
- mi,k|k−1, Pi,k|k−1) and ρi,k|k−1, respectively, where mi,k|k−1=Fi,k|k−1mi,k−1, Pi,k|k−1=Qi,k−1+Fi,k|k−1Pi,k−1(Fi,k|k−1)T, ρi,k|k−1=pS,k(Δtk)ρi,k−1, Fi,k|k−1 is the state transition matrix, superscript T denotes transpose of matrix or vector, Δtk=tk−tk−1 is the time interval between moments k and k−1, Qi,k−1 is the covariance matrix of process noise, pS,k(Δtk) is the surviving probability of the target and
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- a classification step, which uses the predicted marginal distribution and predicted existence probability of each existing target at the current moment to determine whether each measurement in a measurement set originates from the existing targets and split the measurements in the measurement set into two categories:
- a first category and a second category;
- an updating step, which uses the predicted marginal distribution and predicted existence probability of each existing target at the current moment and the first category of measurements as well as the Bayesian rule to obtain an updated marginal distribution and updated existence probability of each existing target at the current moment;
- a pruning and extracting step, which is used to eliminate the target whose existence probability is less than a first threshold, and extract the marginal distribution of the target whose existence probability is greater than a second threshold as the output of the current moment according to the updated marginal distribution and updated existence probability of each existing target at the current moment;
- a generation step, which uses the second category of measurements at the current moment and the second category of measurements at the previous two moments to generate new targets, and estimate the state mean, covariance and marginal distribution of each new target at the current moment by using the least square method;
- a supplement step, which is used to extract the marginal distribution of the new target at the current moment to supplement the output of the current moment, and extract the state estimations of the new target at the previous two moments to supplement the outputs of the previous two moments, respectively;
- a combining step, which is used to combine the marginal distribution and existence probability of the remaining targets after the pruning and extracting step with the marginal distribution and existence probability of the new targets in the generation step, respectively, to form the marginal distribution and existence probability of each target at the current moment, which is used as the input of the next recursion.
38 more elements
A multi-target tracking system applicable to a clutter environment, comprising:
- a prediction module, configured to use marginal distribution and existence probability of each target at the previous moment, and time interval between the current moment and the previous moment to predict marginal distribution and existence probability of each existing target at the current moment;
- wherein k−1 represents the previous moment, k represents the current moment, tk−1 represents the time of the previous moment, tk represents the time of the current moment, and the marginal distribution and existence probability of the target i at moment k−1 are represented as N(xi,k−1;
- mi,k−1, Pi,k−1) and ρi,k−1, respectively, wherein N represents a Gaussian distribution, i=1,2, . . . Nk−1, xi,k−1 is the state vector of the target i at moment k−1, mi, k−1 and Pi, k−1 represent the state mean and covariance of the target i at moment k−1, respectively, and Nk−1 is the total number of the targets at the previous moment;
- based on the marginal distribution N(xi,k−1;
- mi,k−1, Pi,k−1) and the existence probability ρi,k|k−1 of target i at moment k−1, the predicted marginal distribution and existence probability of existing target i at moment k are given by N(xi,k;
- mi,k|k−1, Pi,k|k−1) and ρi,k|k−1, respectively, where mi,k|k−1=Fi,k|k−1mi,k−1, Pi,k|k−1=Qi,k−1+Fi,k|k−1Pi,k−1(Fi,k|k−1)T, ρi,k|k−1=pS,k(Δtk)ρi,k−1, Fi,k|k−1 is the state transition matrix, superscript T denotes transpose of matrix or vector, Δtk=tk−tk−1 is the time interval between moments k and k−1, Qi,k−1 is the covariance matrix of process noise, pS,k(Δtk) is the surviving probability of the target and
- p
- S
- ,
- k
-
- (
- Δ
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- t
- k
- )
- =
- exp
-
- (
- -
- Δ
-
-
- t
- k
- δ
- ·
- T
- )
- ,
- T is the sampling period, δ is a given parameter and i=1,2, . . . Nk−1;
- a classification module, which uses the predicted marginal distribution and predicted existence probability of each existing target at the current moment to determine whether each measurement in the measurement set originates from the existing targets and split the measurements in the measurement set into two categories:
- a first category and a second category;
- an updating module, which uses the predicted marginal distribution and predicted existence probability of each existing target at the current moment and the first category of measurements as well as the Bayesian rule to obtain the updated marginal distribution and updated existence probability of each existing target at the current moment;
- a pruning and extracting module, configured to eliminate the target whose existence probability is less than a first threshold, and extract the marginal distribution of the target whose existence probability is greater than a second threshold as the output of the current moment according to the updated marginal distribution and updated existence probability of each existing target at the current moment;
- a generation module, which uses the second category of measurements at the current moment and the second category of measurements at the previous two moments to generate new targets, and estimate the state mean, covariance and marginal distribution of each new target at the current moment by using the least square method;
- a supplement module, configured to extract the marginal distribution of the new target at the current moment to supplement the output of the current moment, and extract the state estimations of the new target at the previous two moments to supplement the outputs of the previous two moments, respectively;
- and
- a combining module, configured to combine the marginal distribution and existence probability of remaining targets from the pruning and extracting module with the marginal distribution and the existence probability of the new targets generated by the generation module, respectively, to form the marginal distribution and existence probability of each target at the current moment, which is used as the input of the next recursion.
39 more elements
A multi-target tracking system applicable to a clutter environment, comprising a storage medium storing program modules and a processor configured to execute the program modules, the program modules comprising:
- a prediction module, configured to use marginal distribution and existence probability of each target at the previous moment, and time interval between the current moment and the previous moment to predict marginal distribution and existence probability of each existing target at the current moment;
- a classification module, which uses the predicted marginal distribution and predicted existence probability of each existing target at the current moment to determine whether each measurement in the measurement set originates from the existing targets and split the measurements in the measurement set into two categories:
- a first category and a second category;
- an updating module, configured to use the predicted marginal distribution and predicted existence probability of each existing target at the current moment and the first category of measurements as well as the Bayesian rule to obtain the updated marginal distribution and updated existence probability of each existing target at the current moment;
- a pruning and extracting module, configured to eliminate the target whose existence probability is less than a first threshold, and extract the marginal distribution of the target whose existence probability is greater than a second threshold as the output of the current moment according to the updated marginal distribution and updated existence probability of each existing target at the current moment;
- a generation module, configured to use the second category of measurements at the current moment and the second category of measurements at the previous two moments to generate new targets, and estimate the state mean, covariance and marginal distribution of each new target at the current moment by using the least square method;
- a supplement module, configured to extract the marginal distribution of the new target at the current moment to supplement the output of the current moment, and extract the state estimations of the new target at the previous two moments to supplement the outputs of the previous two moments, respectively;
- and
- a combining module, configured to combine the marginal distribution and existence probability of remaining targets from the pruning and extracting module with the marginal distribution and the existence probability of the new targets generated by the generation module, respectively, to form the marginal distribution and existence probability of each target at the current moment, which is used as the input of the next recursion.
6 more elements
Dependencies are inferred from the claim text.