Abstract
A robust object tracking algorithm based on a three-channel Siamese network is proposed for the visual object tracking problem in the context of traffic. By adding the prediction box in the previous frame as the second template, our network obtained the second template to perform secondary prediction on the current frame. Then, in order to make sure whether the model to accurately regress the correct prediction box by the template frame or the previous frame, we design a similarity matching method based on Structure Similarity Index Measure(SSIM). We have also designed a template update method to ensure that the network selects the correct matching box. Our algorithm has a major advantage in robustness over other real-time target tracking algorithms such as SiamRPN.
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