Fast Visual Tracking with Squeeze and Excitation Region Proposal Network

Siamese trackers have achieved significant progress over the past few years. However, the existing methods are either high speed or high performance, and it is difficult for previous Siamese trackers to balance both. In this work, we propose a high-performance yet effective tracker (SiamSERPN), which utilizes MobileNetV2 as the backbone and equips with the proposed squeeze and excitation region proposal network (SERPN). For the SERPN block, we introduce the distance-IoU (DIoU) into the classification and regression branches to remedy the weakness of traditional RPN. Benefiting from the structure of MobileNetV2, we propose a feature aggregation architecture of multi-SERPN blocks to improve performance further. Extensive experiments and comparisons on visual tracking benchmarks, including VOT2016, VOT2018, and GOT-10k, demonstrate that our SiamSERPN can balance speed and performance. Especially on GOT-10k benchmark, our tracker scores 0.604 while running at 75 frames per second (FPS), which is nearly 27 times that of the state-of-the-art tracker.