Shape robust Siamese network tracking based on weakly supervised learning

摘要

This paper combines the boundary box regression with the training data occlusion processing method, the occlusion problem is more accurate and the tracking accuracy is improved. The occlusion problem is now the major challenge in target tracking. This paper puts forward a weakly monitoring framework to address this problem. The main idea is to randomly hide the most discriminating patches in the input images, forcing the network to focus on other relevant parts. Our method only needs to modify the inputs, no need to hide any patches during the test.