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Object tracking algorithm based on contextual visual saliency
Fu, Bao1,2; Peng, Xianrong1
Source PublicationProceedings of SPIE: 8th International Symposium on Advanced Optical Manufacturing and Testing Technology: Optical Test, Measurement Technology, and Equipment
Volume9684Pages:96842O
2016
Language英语
ISSN0277-786X
DOI10.1117/12.2243216
Indexed BySCI ; Ei
WOS IDWOS:000387429500096
SubtypeC
AbstractAs to object tracking, the local context surrounding of the target could provide much effective information for getting a robust tracker. The spatial-Temporal context (STC) learning algorithm proposed recently considers the information of the dense context around the target and has achieved a better performance. However STC only used image intensity as the object appearance model. But this appearance model not enough to deal with complicated tracking scenarios. In this paper, we propose a novel object appearance model learning algorithm. Our approach formulates the spatial-Temporal relationships between the object of interest and its local context based on a Bayesian framework, which models the statistical correlation between high-level features (Circular-Multi-Block Local Binary Pattern) from the target and its surrounding regions. The tracking problem is posed by computing a visual saliency map, and obtaining the best target location by maximizing an object location likelihood function. Extensive experimental results on public benchmark databases show that our algorithm outperforms the original STC algorithm and other state-of-The-Art tracking algorithms. © 2016 SPIE.
KeywordImage Segmentation Manufacture Optical Testing Target Tracking Tracking (Position) Visualization
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ioe.ac.cn/handle/181551/8515
Collection光电探测与信号处理研究室(五室)
Affiliation1. Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu
2.610209, China
3. University of Chinese Academy of Sciences, Beijing
4.100039, China
Recommended Citation
GB/T 7714
Fu, Bao,Peng, Xianrong. Object tracking algorithm based on contextual visual saliency[J]. Proceedings of SPIE: 8th International Symposium on Advanced Optical Manufacturing and Testing Technology: Optical Test, Measurement Technology, and Equipment,2016,9684:96842O.
APA Fu, Bao,&Peng, Xianrong.(2016).Object tracking algorithm based on contextual visual saliency.Proceedings of SPIE: 8th International Symposium on Advanced Optical Manufacturing and Testing Technology: Optical Test, Measurement Technology, and Equipment,9684,96842O.
MLA Fu, Bao,et al."Object tracking algorithm based on contextual visual saliency".Proceedings of SPIE: 8th International Symposium on Advanced Optical Manufacturing and Testing Technology: Optical Test, Measurement Technology, and Equipment 9684(2016):96842O.
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