An airport object tracking method in foggy weather based on Siamese network

Abstract

With the increasing number of passengers and the continuous expansion of the scale of the airport, the security of the airport has gradually attracted people’s attention. Aiming at the security needs of airports in foggy weather, an improved object tracking method based on SiamRPN is proposed, which solves the problems of drift and occlusion in the process of object tracking in foggy weather. First, a video pre-processing module plays the role of processing the input video, improving the target's visibility to be tracked in the video. Then, a global attention module is proposed and introduced in the feature extraction network. The global context information is integrated into the backbone network. The Region Proposal Network (RPN) network performs classification and regression operations and finally calculates the tracking results. The method is tested on a private data set that contains 20 airport videos. The results show that compared with SaimRPN, the improved method has more competitive performance. The AUC of success rate and precision has increased 6.5% and 3.1% compared with SiamRPN.