Visual tracking using structural local DCT sparse appearance model with occlusion detection
| Autor: | M. Omair Ahmad, Mallappa Kumara Swamy, B. K. Shreyamsha Kumar |
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| Rok vydání: | 2018 |
| Předmět: |
Computer Networks and Communications
business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering Pattern recognition 02 engineering and technology Iterative reconstruction Active appearance model Hardware and Architecture Outlier 0202 electrical engineering electronic engineering information engineering Media Technology Benchmark (computing) Discrete cosine transform Eye tracking Artificial intelligence Particle filter business Software |
| Zdroj: | Multimedia Tools and Applications. 78:7243-7266 |
| ISSN: | 1573-7721 1380-7501 |
| DOI: | 10.1007/s11042-018-6453-z |
| Popis: | 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 l1-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. |
| Databáze: | OpenAIRE |
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