Object Tracking With Multi-View Support Vector Machines

How to build an accurate and reliable appearance model to improve the performance is a crucial problem in object tracking. Since the multi-view learning can lead to more accurate and robust representation of the object, in this paper, we propose a novel tracking method via multi-view learning framework by using multiple support vector machines (SVM). The multi-view SVMs tracking method is constructed based on multiple views of features and a novel combination strategy. To realize a comprehensive representation, we select three different types of features, i.e., gray scale value, histogram of oriented gradients (HOG), and local binary pattern (LBP), to train the corresponding SVMs. These features represent the object from the perspectives of description, detection, and recognition, respectively. In order to realize the combination of the SVMs under the multi-view learning framework, we present a novel collaborative strategy with entropy criterion, which is acquired by the confidence distribution of the candidate samples. In addition, to learn the changes of the object and the scenario, we propose a novel update scheme based on subspace evolution strategy. The new scheme can control the model update adaptively and help to address the occlusion problems. We conduct our approach on several public video sequences and the experimental results demonstrate that our method is robust and accurate, and can achieve the state-of-the-art tracking performance.