Siamese Progressive Attention-Guided Fusion Network for Object Tracking

Abstract

<p indent=0mm>For the most of object tracking algorithms using siamese networks, the semantic feature derived from the last layer of the backbone network is used to calculate the similarity. However, the use of single deep feature space often leads to partial loss of effective information. To address this issue, the siamese progressive attention-guided fusion network is proposed. First, the deep and shallow feature information is simultaneously extracted using the backbone network. Second, a top-down strategy is adopted to gradually encode and fuse deep semantic information, as well as shallow spatial structure information is obtained from the progressive feature aggregation module. We then use attention module to reduce feature redundancy that generated by fusion. Last, the optimal solution of object tracking is formed by calculating the similarity between the target and search area. By means of attention module, the tracker can selectively integrate multi-level features information to enhance the performance of the applications. As compared with SiamDW and other traditional methods, experimental results conducted on the five common tracking benchmarks including OTB2013, OTB50, OTB2015, VOT2016 and VOT2017, demonstrate that the effectiveness of the proposed algorithm in terms of tracking accuracy and success rate.