MultiBSP: multi-branch and multi-scale perception object tracking framework based on siamese CNN

Object tracking has achieved impressive performance in computer vision. However, there are many challenges due to complex scenarios in reality. The mainstream trackers mostly locate the object in form of two branches, which limits the ability of trackers to fully mine similarity between template and search region. In this paper, we propose a multi-branch and multi-scale perception object tracking framework based on Siamese Convolutional Neural Networks (MultiBSP), in which the multi-branch tracking framework is established based on the idea of relation mining, and a tower-structured relation network is designed for each branch to learn the non-linear relation function between template and search region. By branch combination, multiple branches can verify their predictions with each other, which is beneficial to robust tracking. Besides, in order to sense the scale and aspect ratio of object in advance, a multi-scale perception module is designed by utilizing the dilated convolutions in five scales, which contributes to the ability of tracker to deal with scale variation. In addition, we propose an information enhancement module that focuses on important features and suppresses unnecessary ones along spatial and channel dimensions. Extensive experiments on six visual tracking benchmarks including OTB100, VOT2018, VOT2019, UAV123, GOT-10k, and LaSOT demonstrate that our MultiBSP can achieve robust tracking and have state-of-the-art performance. Finally, ablation experiments verify the effectiveness of each module and the tracking stability is proved by qualitative and quantitative analyses.