Abstract: Accurate scale estimation poses a challenge in object tracking, with existing methods plagued by high computational complexity, numerous hyperparameters, and low accuracy. To address these issues, this paper proposes a Siamese segmentation network for object tracking utilizing object contours. This network consists of a twin sub-network and a contour segmentation network, offering the advantage of eliminating the need to predefine anchor boxes based on prior knowledge, thereby reducing the number of hyperparameters. Furthermore, a multi-point regression-based object contour tracking method is implemented. This method models object tracking through region classification and contour regression, enabling the simultaneous acquisition of various object states, including upright bounding boxes, rotated bounding boxes, and contours. The tracking process of this method is as follows: first, the Siamese sub-network is used to estimate the initial bounding box of the object; second, the feature vector of the initial bounding box is transformed into an object contour through the contour segmentation network; finally, the final bounding box is fitted based on the object contour. Experimental results on the OTB-2015 (Success=70%), VOT-2020 (EAO=52%), TrackingNet (AUC=78.9%), and LaSOT (AUC=64.1%) datasets demonstrate that the proposed tracking method outperforms existing advanced object tracking methods in terms of tracking performance.

Key words: object tracking, Siamese network, contour segmentation, multipoint regression, scale estimation