Improved target tracking based on spatio-temporal learning

Jia, Songmin; Dishi, Zeng; Xu, Tao; Hui, Zhang; Li, Xiuzhi

doi:10.1109/icinfa.2016.7832118

Cited by 3 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the spatial and temporal characteristics of the target background area are relatively stable, STC tracking algorithm is simple and effective to improve the accuracy of target location by using abundant background information. 16 The algorithm uses the extreme search process of the target's confidence map in the current frame to replace the tracking process. In the first frame, STC algorithm first calculates a confidence map according to the correlation formula and then takes the position of the extreme of the confidence map as the center of the target.…”

Section: Stc Tracking Algorithmmentioning

confidence: 99%

“…Similarly, the feature pyramid of training samples f with the same scale. Finally, the expectation of correlation output is calculated by Gauss function g, and the scale correlation filter is updated according to formula (16). According to the formula (17), the correlation score can be calculated and the maximum value is taken as the new target scale state.…”

Section: The Algorithm Flowmentioning

confidence: 99%

See 1 more Smart Citation

An improved spatio-temporal context tracking algorithm based on scale correlation filter

2019

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Spatio-temporal context algorithm is commonly used in geology and has been introduced into the field of target tracking in recent years. The algorithm improved the robustness of visual tracking through dense contextual information around the target and thus achieved great tracking results. However, updating errors may occur in spatio-temporal context algorithm when the target has rapid changes in scale and appearance, resulting in the algorithm cannot extract the target area accurately and completely. In order to overcome this problem, this article proposes an improved spatio-temporal context algorithm based on scale correlation filter. First of all, the algorithm extracts samples of different scales around the target after the target is settled by spatio-temporal context algorithm and then forms the pyramid of scale characteristics through samples extracted by histogram of oriented gradients operator. Second, the best scale parameter will be achieved by means of scale correlation filter to update the scale model. Finally, the results show that the algorithm has good tracking effect and robustness through two experiments which are contrast experiment before and after the improvement of algorithm and comparison experiment with other advanced algorithms.

show abstract

Section: Stc Tracking Algorithmmentioning

confidence: 99%

Section: The Algorithm Flowmentioning

confidence: 99%

An improved spatio-temporal context tracking algorithm based on scale correlation filter

2019

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…Sparse-grid quadrature nonlinear filtering [10] was proposed to approximate the multidimensional integrals using weighted sparse-grid quadrature points, and such filters could achieve higher accuracy than conventional filters could. To address the problem of low accuracy of the CKF in several practical applications, a square root embedded CKF [11] was proposed, which employed an embedded cubature rule. e spherical simplex radial CKF [12] was proposed to improve the accuracy and efficiency of the traditional CKF; the simplex radial CKF calculated the spherical and radial integrals using a regular simplex, whereas the conventional CKF employed the moment matching method for the same purpose.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Weight Update Algorithm for Target Tracking of UUV Based on Improved Gaussian Mixture Cubature Kalman Filter

Wang

Li³

et al. 2020

Complexity

View full text Add to dashboard Cite

The Gaussian mixture filter can solve the non-Gaussian problem of target tracking in complex environment by the multimode approximation method, but the weights of the Gaussian component of the conventional Gaussian mixture filter are only updated with the arrival of the measurement value in the measurement update stage. When the nonlinear degree of the system is high or the measurement value is missing, the weight of the Gauss component remains unchanged, and the probability density function of the system state cannot be accurately approximated. To solve this problem, this paper proposes an algorithm to update adaptive weights for the Gaussian components of a Gaussian mixture cubature Kalman filter (CKF) in the time update stage. The proposed method approximates the non-Gaussian noise by splitting the system state, process noise, and observation noise into several Gaussian components and updates the weight of the Gaussian components in the time update stage. The method contributes to obtaining a better approximation of the posterior probability density function, which is constrained by the substantial uncertainty associated with the measurements or ambiguity in the model. The estimation accuracy of the proposed algorithm was analyzed using a Taylor expansion. A series of extensive trials was performed to assess the estimation precision corresponding to various algorithms. The results based on the data pertaining to the lake trial of an unmanned underwater vehicle (UUV) demonstrated the superiority of the proposed algorithm in terms of its better accuracy and stability compared to those of conventional tracking algorithms, along with the associated reasonable computational time that could satisfy real-time tracking requirements.

show abstract