Visual Tracking via Nonnegative Regularization Multiple Locality Coding

TL;DR

This paper introduces a novel object tracking approach using a regularization-based approximation of LLC, replacing the non-negativity constraint with an $ ext{l}_2$ norm, and employs multiple dictionaries and occlusion detection for improved robustness.

Contribution

It proposes a new LLC approximation method with $ ext{l}_2$ regularization, multiple local dictionaries, and an occlusion detection strategy for enhanced visual tracking.

Findings

Achieves favorable performance on challenging sequences.

Outperforms several state-of-the-art tracking methods.

Effectively detects occlusions to prevent tracking drift.

Abstract

This paper presents a novel object tracking method based on approximated Locality-constrained Linear Coding (LLC). Rather than using a non-negativity constraint on encoding coefficients to guarantee these elements nonnegative, in this paper, the non-negativity constraint is substituted for a conventional $\ell_2$ norm regularization term in approximated LLC to obtain the similar nonnegative effect. And we provide a detailed and adequate explanation in theoretical analysis to clarify the rationality of this replacement. Instead of specifying fixed K nearest neighbors to construct the local dictionary, a series of different dictionaries with pre-defined numbers of nearest neighbors are selected. Weights of these various dictionaries are also learned from approximated LLC in the similar framework. In order to alleviate tracking drifts, we propose a simple and efficient occlusion detection method. The occlusion detection criterion mainly depends on whether negative templates are selected to represent the severe occluded target. Both qualitative and quantitative evaluations on several challenging sequences show that the proposed tracking algorithm achieves favorable performance compared with other state-of-the-art methods.