Object tracking algorithm based on dynamic weight in dual branch siamese network

Abstract

Abstract: Some of siamese tracking algorithms represented by SiamFC are designed for target appearance information.However, appearance information is easily affected by factors such as high-speed movement, motion blur, and illumination changes, resulting in tracking drift or target loss. In order to improve the ability of the algorithm to adapt target appearance changes, this paper adopts a dual -branch siamese network tracking algorithm based on dynamic weights. Based on improved SiamFC algorithm as the appearance branch, a semantic branch using dual attention mechanism to enhance information extraction is added as an effective supplement. In the tracking stage, a dynamic weight is used to fuse the tracking results of the two branches, which effectively suppresses the influence of target appearance changes, also improves the tracking accuracy and robustness of the algorithm. Our algorithm has been validated on four standard object tracking data sets (OTB2015, UAV123, UAV20L, GOT-10k). The average tracking frame rate is 47 frames/s, which meets the real-time tracking requirements.

Key words: video object tracking, siamese network, attention mechanism, dynamic weight

CLC Number:

TP391.41

HAN Ping , WANG Haowei , FANG Cheng. Object tracking algorithm based on dynamic weight in dual branch siamese network[J]. Journal of Civil Aviation University of China, 2022, 40(5): 15-22.

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