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Journal of System Simulation

Abstract

Abstract: In view of the fact that the background perception algorithm does not establish a relationship with the spatio-temporal domain characteristics of the target, and cannot accurately deal with the occlusion, deformation and other abnormal tracking, a object tracking algorithm which can adaptively perceive the spatio-temporal anomalies is proposed. In the training stage of correlation filter, the adaptive spatial regularization term is introduced to establish a relationship with the spatio-temporal characteristics of sample. The abnormal perception method is proposed according to the peak value of response map. Taking advantage of the different confidence of historical filter and the continuity of target in the time domain, the historical filter with high confidence is adaptively selected as the reference template of time regularization through the abnormal perception method, which reduces the risk of filter degradation. Simulation experiments carried out on OTB50, OTB100 and TC128 test benchmarks show that the algorithm can adapt to the tracking tasks under complex scenarios such as appearance changes and messy pictures, and has strong robustness and practicability.

First Page

338

Last Page

351

CLC

TP391.4

Recommended Citation

Qiu Yunfei, Bu Xiangrui, Zhang Boqiang. Dynamic Spatio-temporal Anomaly-aware Correlation Filtering Object Tracking Algorithm[J]. Journal of System Simulation, 2024, 36(2): 338-351.

Corresponding Author

Bu Xiangrui

DOI

10.16182/j.issn1004731x.joss.22-1047

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