Visual tracking via adaptive structural local sparse appearance model

TLDR

Sparse representation has been used for visual tracking by selecting candidates with minimal reconstruction error, but most trackers rely on holistic templates and ignore sparse coefficients, making them vulnerable to similar objects and occlusion. This work proposes a simple yet robust tracking method based on a structural local sparse appearance model. The method combines alignment‑pooling of local patches to capture partial and spatial target information, improving localization and occlusion handling, and employs a template‑update strategy that merges incremental subspace learning with sparse representation to adapt to appearance changes while reducing drift. Experiments on challenging benchmark sequences show the proposed tracker outperforms several state‑of‑the‑art methods.

Abstract

Sparse representation has been applied to visual tracking by finding the best candidate with minimal reconstruction error using target templates. However most sparse representation based trackers only consider the holistic representation and do not make full use of the sparse coefficients to discriminate between the target and the background, and hence may fail with more possibility when there is similar object or occlusion in the scene. In this paper we develop a simple yet robust tracking method based on the structural local sparse appearance model. This representation exploits both partial information and spatial information of the target based on a novel alignment-pooling method. The similarity obtained by pooling across the local patches helps not only locate the target more accurately but also handle occlusion. In addition, we employ a template update strategy which combines incremental subspace learning and sparse representation. This strategy adapts the template to the appearance change of the target with less possibility of drifting and reduces the influence of the occluded target template as well. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.