Tracking by Third-Order Tensor Representation

This paper proposes a robust tracking algorithm by third-order tensor representation and adaptive appearance modeling. In this method, the target in each video frame is represented by a third-order tensor. This representation preserves the spatial correlation inside the target region and can integrate multiple appearance cues for target description. Based on this representation, a multilinear subspace is learned online to model the target appearance variations during tracking. Compared to other methods, our approach can detect local spatial structure in the target tensor space and fuse information from different feature spaces. Therefore, the learned appearance model is more discriminative when there are significant appearance variations of the target or when the background gets cluttered. Applying the multilinear algebra, our appearance model can efficiently be learned and updated online, without causing high-dimensional data-learning problems. Then, tracking is implemented in the Bayesian inference framework, where a likelihood model is defined to measure the similarity between a test sample and the learned appearance model, and a particle filter is used to recursively estimate the target state over time. Theoretic analysis and experiments compared with other state-of-the-art methods demonstrate the effectiveness of the proposed approach.