Cross-Layer Convolutional Siamese Network for Visual Tracking

Yanyin Chen, Xing Chen, Huibin Tan, Xiang Zhang, Long Lan, Xuhui Huang, Zhigang Luo

Abstract: In most trackers for visual tracking, Siamese network based trackers construct a pair of twin structures to learn a similarity metric between tracked object and search region to predict the position of the object in the coming frame. They have achieved impressive performance in both speed and accuracy. However, semantic features from different layers are not fully explored in most current Siamese network based tracker. To this, we propose a cross-layer convolutional Siamese network tracker (Siam-CC) which attempts to explore more semantic features of different layers from two aspects. Firstly, we combine the shallow-to-deep cross-layer convolutional response maps to capture various semantic-aware features and meanwhile enforce Siam-CC to only focus on the most interesting location, because much more semantic information is able to reduce negative effect of background. Secondly, to further boost the discrimination of responses, an adaptive contrastive loss is additionally developed together with traditional logistical loss, which, to some extent, assists in filtering out some noisy responses. Experiments on a large-scale benchmark dataset show the effectiveness of Siam-CC as compared to the state-of-the-art trackers.