4.6 Article

Visual tracking using structural local DCT sparse appearance model with occlusion detection

Journal

MULTIMEDIA TOOLS AND APPLICATIONS
Volume 78, Issue 6, Pages 7243-7266

Publisher

SPRINGER
DOI: 10.1007/s11042-018-6453-z

Keywords

Visual tracking; Local DCT sparse appearance model; Holistic image reconstruction; Reconstruction error; Occlusion map; Observation model update

Funding

  1. Natural Sciences and Engineering Research Council (NSERC) of Canada
  2. Regroupement Strategique en Microsystemes du Quebec (ReSMiQ)
  3. Ministere de l'Education, de l'Enseignement Superieur et de la Recherche (MEESR) du Quebec

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In this paper, a structural local DCT sparse appearance model with occlusion detection is proposed for visual tracking in a particle filter framework. The energy compaction property of the 2D-DCT is exploited to reduce the size of the dictionary as well as that of the candidate samples so that the computational cost of l(1)-minimization can be lowered. Further, a holistic image reconstruction procedure is proposed for robust occlusion detection and used for appearance model update, thus avoiding the degradation of the appearance model in the presence of occlusion/outliers. Also, a patch occlusion ratio is introduced in the confidence score computation to enhance the tracking performance. Quantitative and qualitative performance evaluations on two popular benchmark datasets demonstrate that the proposed tracking algorithm generally outperforms several state-of-the-art methods.

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