R-SiamNet: ROI-Align Pooling Baesd Siamese Network for Object Tracking

Recently, deep Siamese network-based trackers have achieved superior performance in the visual tracking community. Siamese trackers formulate the visual tracking problem as learning the similarity metric by cross-correlation between the target template and the search region. However, previous Siamese trackers are still susceptible to the distractors, mainly due to two reasons: (1)template region contains non-target information; (2)insufficient distractor learning in template frame. In this paper, we comprehensively combine the ROI align pooling with the Siamese Network (named R-SiamNet). Due to accurate region pooling operator, we present simple yet effective multi-distractors learning for template, which imposes the discriminative of feature embedding. The experimental results show that our R-SiamNet outperforms the state-of-the-art trackers on VOT2016 [1], VOT2018 [2] and OTB100 [3] datasets.