Discriminative metric learning for shape variation object tracking

It is a challenging task to track a shape variation object. In this paper, a novel discriminative metric learning based on multi-features appearance model is proposed for shape variation object tracking. Initially, we exploit the shape invariant properties and form multi-features appearance model, which consists of hue features, center-symmetric local binary pattern (CSLBP) at multiple scales, and orientation features. With the obtained multi-features appearance descriptor, we propose an improved bias discriminative component analysis (BDCA) classifier to distinguish the target object and background. In addition, a novel Mahalanobis distance metric is learned by BDCA classifier, which project the original space into a new space. Furthermore, based on the learned distance metric, the tracked object can be located in the new transformed feature space by matching the candidate image regions with templates in library. Compared with several other tracking algorithms, the experimental results demonstrate that the proposed algorithm is able to track an object accurately especially for object pose change, rotation and occlusion.