Object tracking is a basic research issue in the field of computer vision.With the development oftracking technology,existing trackers mainly have two challenges,namely relying on a large amount of data annotation information and tracking drift,which seriously limits the improvement of tracker performance.In order to overcome the above challenges,unsupervised target tracking and hidden space matching methods are proposed.Firstly,image pairs are generated in the foreground via a correctable optical flow method.Secondly,the generated image pairs are utilized to train the siamese tracker from scratch.Finally,the hidden space matching method is used to solve the problem of losing track when the target deforms greatly,is occluded,goes out of the field of view and drifting.Experimental results show that the algorithm UHOT significantly improves on multiple datasets and demonstrates strong robustness in difficult scenarios.Compared with the latest unsupervised algorithm SiamDF,UHOT gaines 8％gain on the VOT dataset,comparable to state-of-the-art supervised siamese trackers.
维普
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