Single and multiple object tracking using a multi-feature joint sparse representation

Hu, W. and Li, W. and Zhang, X. and Maybank, Stephen J. (2014) Single and multiple object tracking using a multi-feature joint sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 37 (4), pp. 816-833. ISSN 0162-8828.

Abstract

In this paper, we propose a tracking algorithm based on a multi-feature joint sparse representation. The templates for the sparse representation can include pixel values, textures, and edges. In the multi-feature joint optimization, noise or occlusion is dealt with using a set of trivial templates. A sparse weight constraint is introduced to dynamically select the relevant templates from the full set of templates. A variance ratio measure is adopted to adaptively adjust the weights of different features. The multi-feature template set is updated adaptively. We further propose an algorithm for tracking multi-objects with occlusion handling based on the multi-feature joint sparse reconstruction. The observation model based on sparse reconstruction automatically focuses on the visible parts of an occluded object by using the information in the trivial templates. The multi-object tracking is simplified into a joint Bayesian inference. The experimental results show the superiority of our algorithm over several state-of-the-art tracking algorithms.

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