Real-Time Visual Tracking with Compact Shape and Color Feature

The colour feature is often used in the object tracking. The tracking methods extract the colour features of the object and the background, and distinguish them by the classifier. However, these existing methods simply use the colour information of the target pixel and does not consider the shape feature of the target, so that the description capability of the feature is weak. Moreover, incorporating shape information often leads to large feature dimension, which is not conducive to real-time object tracking. Recently, the emergence of visual tracking methods based on deep learning has also greatly increased the demand of computing resources for the algorithm. In this paper, we propose a real-time visual tracking method with compact shape and colour feature, which forms low dimensional compact shape and colour feature by fusing the shape and colour characteristics of the candidate object region, and reduces the dimensionality of the combined feature through the Hash function. The structural classification function is trained and updated online with dynamic data flow for adapting to the new frames. Further, the classification and prediction of the object are carried out with structured classification function. The experimental results demonstrate that the proposed tracker performs superiorly against several state-of-the-art algorithms on the challenging benchmark dataset OTB-100 and OTB-13.