Siamese Multi-attention Network-based Approach to Tracking of Light Object Intrusion into Overhead Contact System

A new method based on siamese multi⁃attention network was proposed to address the issues such as large scale variation of light objects intruding overhead contact system, occlusion interference and busy background of railroad clearance that may cause tracking failure. Three attention mechanisms were introduced to extract the flicker features from a deeper level, eliminate the local perceptual field restriction by spatial attention, highlight the channel features of flicker category by channel attention, focus the cross⁃attention on the contextual relationship between the target template and the search image, and suppress the background interference by spatial regularization filter before finally fusing the features of each part to achieve the tracking of the intruding flicker. Accuracy and accuracy experiments were conducted using the OTB100 dataset, and the data collected from the test line of the State Key Laboratory were used as arithmetic examples for experiments. The effectiveness of the new method was verified by ablation experiments. The results show that the new method can obtain better robustness and accuracy compared with the correlation filtering class SRDCF algorithm and the deep learning classes SiamRPN++ and DaSiamRPN algorithms. © 2024 Science Press. All rights reserved.