Double Adjust Head Siamese Network for Object Tracking.

Siamese network based trackers formulate tracking as a similarity matching problem between a target template and a search region. Virtually all popular Siamese trackers use cross-correlation to measure the similarity between the feature of template and search image. The emphasis for feature extraction in different regions (inside and contours) are the same. Besides, the global matching also seriously neglects the part-level information and the deformation of targets during tracking. In this paper, a simple but effective Double Adjust Head Siamese Network is proposed to extract features from an object inside and object contours respectively. A Pixelwise Cross- correlation model (PWC) is designed to solve the problem caused by the fixed template structure in conventional correlation operations. Compared with baseline algorithm, the AO, SR0.5, SR0.75 of the proposed algorithm on GOT10k dataset are increased by 3.4%, 7.0% and 2.3%. Running at over 90 frames per second on RTX 2080Ti GPU. [ABSTRACT FROM AUTHOR]