Multi-stage analysis for long-term tracking

Lizhi Bai; Liyan Zhang

doi:10.1117/12.2589391

27 January 2021 Multi-stage analysis for long-term tracking

Lizhi Bai, Liyan Zhang

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 117200I (2021) https://doi.org/10.1117/12.2589391
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

In long-term visual object tracking, the tracking model would be prone to drift or corruption and the tracker can hardly catch the target again after tracking failures. A set of novel strategies for long-term tracking is proposed to solve these problems. First, a simple and efficient method is proposed to calculate the tracking confidence of Staple, a well-known tracker based on correlation filers. A model update mechanism is then developed to prevent model corruption. Furthermore, an online Support Vector Machine (SVM) classifier is trained to re-detect the object in case of unreliable tracking result. By means of intermittent sampling in the re-detection stage, the computational efficiency and the re-detection reliability are greatly improved. The combination of these new components in multi-stages spawns a real-time, accurate and robust tracker for long-term video. Experimental results demonstrate that our tracker, operating at a speed of 30 FPS, performs superiorly against some competitive trackers on robustness and accuracy, especially when the target encounters occlusion, severe deformation and out-of-view.

Citation Download Citation

Lizhi Bai and Liyan Zhang "Multi-stage analysis for long-term tracking", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 117200I (27 January 2021); https://doi.org/10.1117/12.2589391

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