Visual Tracking Based on Siamese Network of Fused Score Map

Nowadays, visual object tracking becomes a hotspot and difficulty to achieve a real-time and accurate target tracking, but the Siamese network has solved these difficulties because of its good tracking effect and real-time performance. The location of the target in the previous frame is the template, and the similarity matching is carried out in the search area of the current frame. However, it uses Alexnet network with simple structure and fewer layers to extract features, and just uses a score map to predict the final position of the object. Aiming at these problems, in this paper, we propose the Siamese network of fused response map that use the Alexnet network with fine tuning to extract target features, and weight fusion of score maps to estimate the final position of object. Sufficient experiments on the VOT2015 and OTB100 benchmarks validate that our tracker can improve tracking performance, and perform at 60FPS.