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First Step toward Model-Free, Anonymous Object Tracking with Recurrent Neural Networks

19 Nov 2015 • Quan Gan • Qipeng Guo • Zheng Zhang • Kyunghyun Cho

In this paper, we propose and study a novel visual object tracking approach based on convolutional networks and recurrent networks. The proposed approach is distinct from the existing approaches to visual object tracking, such as filtering-based ones and tracking-by-detection ones, in the sense that the tracking system is explicitly trained off-line to track anonymous objects in a noisy environment... (read more)

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