| Autor: |
Guangnan Zhang, Jinlong Yang, Weixing Wang, Yu Hen Hu, Jianjun Liu |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
EURASIP Journal on Image and Video Processing, Vol 2019, Iss 1, Pp 1-12 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
1687-5281 |
| DOI: |
10.1186/s13640-019-0411-1 |
| Popis: |
Abstract We propose a high-performance visual target tracking (VTT) algorithm based on classified-patch kernel particle filter (CKPF). Novel features of this VTT algorithm include sparse representations of the target template using the label-consistent K-singular value decomposition (LC-KSVD) algorithm; Gaussian kernel density particle filter to facilitate candidate template generation and likelihood matching score evaluation; and an occlusion detection method using sparse coefficient histogram (ASCH). Experimental results validate superior performance of the proposed tracking algorithm over state-of-the-art visual target tracking algorithms in scenarios that include occlusion, background clutter, illumination change, target rotation, and scale changes. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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