Real-Time Siamese Visual Tracking with Lightweight Transformer
Dinh Thang Hoang, Trung Kien Thai, Thanh Nguyen Chi, Long Quoc Trany · 2021 8th NAFOSTED Conference on Information and Computer Science (NICS) · 2021
Trackers based on Siamese have demonstrated more remarkable performance in visual tracking. The majority of existing trackers typically compute target template and search image features independently, then utilize cross-correlation to predict the possibility of an object appearing at each spatial position in the search image for target localization. This paper proposes a Siamese network for feature enhancement and aggregation between the target template and the search image by utilizing a lightweight transformer with several linear self- and cross-attention layers. With anchor-free head prediction, the suggested framework is simple and effective. Extensive experiments on visual tracking benchmarks such as VOT2018, UAV123, and OTB100 demonstrates that our tracker achieves state-of-the-art performance and operates at a real-time frame rate of 39 fps.