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http://dx.doi.org/10.14372/IEMEK.2016.11.4.219

Visual object tracking using inter-frame correlation of convolutional feature maps  

Kim, Min-Ji (Chonbuk National University)
Kim, Sungchan (Chonbuk National University)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.11, no.4, 2016 , pp. 219-225 More about this Journal
Abstract
Visual object tracking is one of the key tasks in computer vision. Robust trackers should address challenging issues such as fast motion, deformation, occlusion and so on. In this paper, we therefore propose a visual object tracking method that exploits inter-frame correlations of convolutional feature maps in Convolutional Neural Net (ConvNet). The proposed method predicts the location of a target by considering inter-frame spatial correlation between target location proposals in the present frame and its location in the previous frame. The experimental results show that the proposed algorithm outperforms the state-of-the-art work especially in hard-to-track sequences.
Keywords
Computer vision; tracking; Convolutional neural net; Feature map correlation;
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