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Single and Multiple Object Tracking Using a Multi-Feature Joint Sparse Representation
Hu, Weiming1; Li, Wei1; Zhang, Xiaoqin1; Maybank, Stephen2
Source PublicationIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
2015-04-01
Volume37Issue:4Pages:816-833
SubtypeArticle
AbstractIn this paper, we propose a tracking algorithm based on a multi-feature joint sparse representation. The templates for the sparse representation can include pixel values, textures, and edges. In the multi-feature joint optimization, noise or occlusion is dealt with using a set of trivial templates. A sparse weight constraint is introduced to dynamically select the relevant templates from the full set of templates. A variance ratio measure is adopted to adaptively adjust the weights of different features. The multi-feature template set is updated adaptively. We further propose an algorithm for tracking multi-objects with occlusion handling based on the multi-feature joint sparse reconstruction. The observation model based on sparse reconstruction automatically focuses on the visible parts of an occluded object by using the information in the trivial templates. The multi-object tracking is simplified into a joint Bayesian inference. The experimental results show the superiority of our algorithm over several state-of-the-art tracking algorithms.
KeywordVisual Object Tracking Tracking Multi-objects Under Occlusions Multi-feature Joint Sparse Representation
WOS HeadingsScience & Technology ; Technology
WOS KeywordVISUAL TRACKING ; ROBUST ; MODELS ; COLOR ; OPTIMIZATION ; RECOGNITION ; CONTEXT ; FILTER
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000351213400009
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/8089
Collection模式识别国家重点实验室_视频内容安全
Affiliation1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
2.Univ London Birkbeck Coll, Dept Comp Sci & Informat Syst, London WC1E 7HX, England
Recommended Citation
GB/T 7714
Hu, Weiming,Li, Wei,Zhang, Xiaoqin,et al. Single and Multiple Object Tracking Using a Multi-Feature Joint Sparse Representation[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2015,37(4):816-833.
APA Hu, Weiming,Li, Wei,Zhang, Xiaoqin,&Maybank, Stephen.(2015).Single and Multiple Object Tracking Using a Multi-Feature Joint Sparse Representation.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,37(4),816-833.
MLA Hu, Weiming,et al."Single and Multiple Object Tracking Using a Multi-Feature Joint Sparse Representation".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 37.4(2015):816-833.
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