HU Zhaohua, LI Qi, HAN Qing. Dual-detector system for Long-term tracking[J]. Microelectronics & Computer, 2021, 38(9): 31-37.
Citation: HU Zhaohua, LI Qi, HAN Qing. Dual-detector system for Long-term tracking[J]. Microelectronics & Computer, 2021, 38(9): 31-37.

Dual-detector system for Long-term tracking

  • Aiming to solve the problem of target drift that occurs often in the process of long-term tracking, a dual-detector system for long-term tracking is proposed based on the spatially regularized correlation filters algorithm. In order to adapt to different tracking scenarios and increase the robustness of the model, SVM detector and siamese network detector are added, and the more efficient SVM detector is selected as the main detector. Only when the main detector fails to detect the target, the more accurate siamese network detector is started to detect the target. Through the cooperative work of the two detectors, the current image can be re-detected with high quality. At the same time, the adaptive spatial regularization term is also added in this paper, which can effectively suppress the influence of the boundary effect. The objective function is optimized by the alternating direction multiplier method to reduce the computational complexity and save running time. The experiment on datasets OTB-2013 and OTB-2015 show that the proposed algorithm has strong robustness in many characteristics. Especially in long-term tracking, the tracking distance accuracy and success rate of the proposed algorithm are improved by 12.9% and 6.2% respectively compared with the basic algorithm.
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