Multimed Tools Appl https://doi.org/10.1007/s11042-018-6132-0 Robust object tracking using a sparse coadjutant observation model 1 1 1 Jianwei Zhao · Weidong Zhang · Feilong Cao Received: 17 November 2017 / Revised: 1 May 2018 / Accepted: 9 May 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract This paper develops a classical visual tracker that is called a discriminative sparse similarity (DSS) tracker. Based on the classical Laplacian multi-task reverse sparse repre- sentation to get a DSS map in the DSS tracker, we introduce a sparse generative model (SGM) to handle the appearance variation in the DSS tracker. With the alliance of the DSS map and the SGM, our proposed method can track the object under the occlusion and appearance variations effectively. Numerous experiments on various challenging videos of a tracking benchmark illustrate that the proposed tracker performs favorably against several state-of-the-art trackers. Keywords Object tracking · Sparse representation · Observation model · Discriminative score model · Generative model 1 Introduction As a hot topic in computer vision, object tracking has played an important role in numerous practical applications, such as activity recognition [19–21], human motion [4, 18]visual surveillance, traffic monitoring, and so on. Although some breakthroughs
Multimedia Tools and Applications – Springer Journals
Published: Jun 4, 2018
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