Abstract
Feature representation is at the core of visual tracking. This paper presents a robust tracking method in RGB-D videos. Firstly, the RGB and depth images are separately encoded using a hierarchical convolutional neural network (CNN) features. Secondly, in order to reduce computation cost, we exploit random projection to compress the CNN features. The high dimensional CNN features are randomly projected into a low dimensional feature space. The correlation filter tracking framework is then independently carried out in RGB and depth images. And backward tracking scheme is adopted to evaluate the tracking results in these two images. The final position is determined according to the tracked location in the two image channels. In addition, model updating is implemented adaptively. Our tracker is evaluated on two RGB-D benchmark datasets and achieves comparable results to the other state-of-the-art RGB-D tracking methods.
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