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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Robust Visual Tracking Using Oriented Gradient Convolution Networks
verfasst von : Qi Xu, Huabin Wang, Jian Zhou, Liang Tao
Erschienen in: Computer Vision
Verlag: Springer Singapore
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Abstract
Convolutional networks have been successfully applied to visual tracking to extract some useful feature. However, deep networks are time-consuming to offline training and usually extract the feature from raw pixels. In this paper, we propose a two-layer convolutional network based on oriented gradient. The first layer is constructed by the convolution of the filter and an input image of oriented gradient, which is robust to the illumination variation and motion blur. Then, all of the feature maps of the simple layer are stacked to a complex feature map as the target representation. The complex feature map can encode the local structure feature which is robust to occlusion. The proposed approach is tested on nine challenging sequences in comparison with nine state-of-art trackers, and the result show that the proposed tracker achieves mean overlap rate of 0.75, which outperforms the secondary tracker by 26%.