Improved Fully Convolutional Siamese Networks for Visual Object Tracking Based on Response Behaviour Analysis

Siamese networks have recently attracted significant attention in the visual tracking community due to their balanced accuracy and speed. However, as a result of the non-update of the appearance model and the changing appearance of the target, the problem of tracking drift is a regular occurrence, particularly in background clutter scenarios. As a means of addressing this problem, this paper proposes an improved fully convolutional Siamese tracker that is based on response behaviour analysis (SiamFC-RBA). Firstly, the response map of the SiamFC is normalised to an 8-bit grey image, and the isohypse contours that represent the candidate target region are generated through thresholding. Secondly, the dynamic behaviour of the contours is analysed in order to check if there are distractors approaching the tracked target. Finally, a peak switching strategy is used as a means of determining the real tracking position of all candidates. Extensive experiments conducted on visual tracking benchmarks, including OTB100, GOT-10k and LaSOT, demonstrated that the proposed tracker outperformed the compared trackers such as DaSiamRPN, SiamRPN, SiamFC, CSK, CFNet and Staple and achieved state-of-the-art performance. In addition, the response behaviour analysis module was embedded into DiMP, with the experimental results showing the performance of the tracker to be improved through the use of the proposed architecture.