SiamFF: Visual Tracking with a Siamese Network Combining Information Fusion with Rectangular Window Filtering

Abstract

Recently, Siamese trackers have shown excellent performance in both accuracy and speed. However, traditional trackers have poor robustness against similar objects due to the use of single deep features and the limitation of cosine windows. In this paper, a novel Siamese network combining information fusion with rectangular window filtering named SiamFF is introduced. First, a multilevel fusion network is proposed. At feature-level, the shallow and deep features of the network are fused through a layer-hopping connection to obtain complementary feature maps. Then, the score maps generated by the complementary feature maps are further fused at the score-level to improve the robustness. In addition, based on the continuity and stationarity of objects movement in reality, a score map filtering strategy is proposed. The relative displacement of the target can be predicted by obtaining the interframe information, and the moving direction is applied to filter the score map to further eliminate the analog interference. Experimental results on OTB2015 and VOT2016 benchmarks indicate that SiamFF performs favorably against many state-of-the-art trackers in terms of accuracy while maintaining real-time tracking speed.