Graph-Regularized Structured Support Vector Machine for Object Tracking

How to build a robust and accurate appearance model is a crucial problem in object tracking. However, in most existing tracking methods, the structures among the adjacent video frames and neighboring regions, which may be helpful to improve the representation capability of the appearance model, have not been fully exploited. In this paper, we propose a novel tracking method by taking into account these structures to represent the appearance model. First, we propose a novel graph-regularized structured support vector machine (GS-SVM) algorithm by combining manifold learning and structured learning. Then, the proposed GS-SVM algorithm is employed to build the appearance model and a novel tracking method is developed. This new tracker not only absorbs the advantage of structured learning that deals with the intermediate classification step existing in tracking-by-detection methods, but also exploits the geometry structures in the tracked results and neighboring regions. In addition, a hybrid update strategy is introduced to fit with the GS-SVM-based appearance model. The experimental results demonstrate that the proposed tracking algorithm can outperform several state-of-the-art tracking methods in the benchmark dataset.