Object tracking algorithm based on contextual visual saliency

Bao Fu; XianRong Peng

doi:10.1117/12.2243216

27 September 2016 Object tracking algorithm based on contextual visual saliency

Bao Fu, XianRong Peng

Proceedings Volume 9684, 8th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test, Measurement Technology, and Equipment; 96842O (2016) https://doi.org/10.1117/12.2243216
Event: Eighth International Symposium on Advanced Optical Manufacturing and Testing Technology (AOMATT2016), 2016, Suzhou, China

Abstract

As 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.

Citation Download Citation

Bao Fu and XianRong Peng "Object tracking algorithm based on contextual visual saliency", Proc. SPIE 9684, 8th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test, Measurement Technology, and Equipment, 96842O (27 September 2016); https://doi.org/10.1117/12.2243216

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KEYWORDS

Detection and tracking algorithms

Binary data

Optical tracking

Visualization

Video

Statistical modeling

Visual process modeling

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