Target Tracking Algorithm Based on Mixed Attention and Siamese Network

Abstract

Siamese convolutional neural network, which is a classic framework for object tracking, has received extensive attention from the research community. The method uses a convolutional neural network to obtain target features and matches them with the search area features to achieve target tracking. Aiming at the problems that multi-layer features are difficult to extract effectively and network model parameters are complex, a target tracking algorithm (MA-SiamRPN++) with mixed attention mechanism is proposed based on SiamRPN++. Firstly, the channel attention mechanism is inserted into the backbone network, and then the output features of the channel attention network are fed into the spatial attention network, so as to improve the efficiency of feature extraction in different convolution layers by using mixed attention. At the same time, the deep cross -correlation network is used to better retain the feature information that is conducive to tracking and reduce the parameter complexity of the network to maintain the tracking speed. Finally, experiments on OTB100, VOT2016, and the long-term tracking dataset LaSOT show that the tracker proposed in this paper achieves higher accuracy and success rate than other state-of-the-art trackers.