Similarity perception siamese network for real-time object tracking

Improving the accuracy while maintaining the real-time performance of object tracking is a major challenge for computer vision field. In this paper, an improved Similarity-Perception-Siamese (SP-Siam) network tracking algorithm based on SiamFC is proposed. The algorithm introduces squeeze-and-excitation (SE) block and residual network for similarity map based on Siamese network, adaptively recalibrates the channel characteristic response of similarity map between target and the search inputs by explicitly modeling the interdependence between channels. This study also verifies the network performance on Object Tracking Benchmark (OTB) tracking datasets. The experimental results show that the squeeze-and-excitation block of similarity map has brought significant performance improvement to the existing Siamese network at slight additional computational cost achieved the goal of improving network performance.