Effective sparse tracking with convolution-based discriminative sparse appearance model

Abstract

Existing sparse appearance models, which rely on a sparse linear combination of dictionary atoms, often fall short in leveraging the hierarchical features within the foreground region and the discriminative features that distinguish the foreground from the background. To address these limitations, we propose a novel sparse appearance model called the Convolutional Discriminative Sparse Appearance (CDSA) model. Unlike existing sparse appearance models, the CDSA model is constructed by convolving a set of sparse filters with input images. These filters are designed to highlight the distinctions between foreground and background regions, making the CDSA model discriminative. Additionally, by stacking the convolutional feature maps, the CDSA model captures hierarchical features within the target object. We also propose a robust updating scheme that leverages high-confidence tracking results to mitigate model corruption due to severe occlusion. Extensive experiments on the OTB100 and UAV123@10_fps datasets demonstrate that the proposed CDSA-based sparse tracker outperforms existing sparse trackers and several state-of-the-art tracking methods in terms of tracking accuracy and robustness.