Real-Time Object Tracking Algorithm Based on Siamese Network

Abstract

Object tracking is aimed at tracking a given target that is only specified in the first frame. Due to the rapid movement and the interference of cluttered backgrounds, object tracking is a significant challenging issue in computer vision. This research put forward an innovative feature pyramid and optical flow estimation based on the Siamese network for object tracking, which is called SiamFP. The SiamFP jointly trains the optical flow and the tracking task under the Siamese network framework. We employ the optical flow network based on the pyramid correlation mapping to evaluate the movement information of the target in two contiguous frames, to increase the accuracy of the feature representation. Simultaneously, we adopt spatial attention as well as channel attention to effectively restrain the ambient noise, stress the target area, and better extract the features of the given object, so that the tracking algorithm has a higher success rate. The proposed SiamFP obtains state-of-the-art performance on OTB50, OTB2015, and VOT2016 benchmarks while exhibiting better real-time and robustness.