Object Tracking Using Sparsity Based Discriminative Minimum-Spanning-Tree

Abstract

An object tracking method based on a minimum-spanning-tree one-class classifier is proposed where the background is regarded as a collection of all other classes except the target. First, two kinds of different target class examples and outlier examples are collected in the recent few frames. Meanwhile, the candidates are sampled by a particle filter in the current frame. Second, using the sparsity-based local discriminant analysis, all examples and candidates are projected into a local discriminative subspace to improve the discriminative ability of one-class classifier. Then, a graph is built on all the target examples by adopting one-to-all sparse reconstruction coefficients rather than pair-wise Euclidean distance and a minimum-spanning-tree one-class classifier is trained on the graph. Finally, the similarity scores of the candidates are evaluated by the trained classifier. The one with the highest score is determined as the tracking result and used to update the training set. The experimental results demonstrate the superior performance of our method in robustness and accuracy to the state-of-the-art methods.