Part Context Learning for Visual Tracking

Guibo Zhu, Jinqiao Wang, Chaoyang Zhao and Hanqing Lu

In Proceedings British Machine Vision Conference 2014


Context information is widely used in computer vision for tracking arbitrary objects. Most existing works focus on how to distinguish the tracked object from background or inter-frame object similarity information or key-points supporters as their auxiliary information to assist them in tracking. However, in most cases, how to discover and represent both the intrinsic property inside the object and surrounding information is still an open problem. In this paper, we propose a unified context learning framework that can capture stable structure relations of in-object parts, context parts and the tracked object to enhance the tracker´┐Żs performance. The proposed Part Context Tracker (PCT) consists of an appearance model, an internal relation model and an context relation model. The appearance model represents the appearances of the object and parts. The internal relation model utilizes the parts inside the object to describe the spatial-temporal structure property directly, while the context relation model takes advantage of the latent intersection between the object and background parts. Then the appearance model, internal relation model and context relation model are embedded in a max-margin structured learning framework. Furthermore, a simple yet robust update strategy using median filter is utilized, which can deal with appearance change effectively and alleviate the drift problem. Extensive experiments are conducted on various benchmark videos, and the comparisons with state-of-the-arts demonstrate the effectiveness of our work.




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Bibtex File



Guibo Zhu, Jinqiao Wang, Chaoyang Zhao, and Hanqing Lu. Part Context Learning for Visual Tracking. Proceedings of the British Machine Vision Conference. BMVA Press, September 2014.


	title = {Part Context Learning for Visual Tracking},
	author = {Zhu, Guibo and Wang, Jinqiao and Zhao, Chaoyang and Lu, Hanqing},
	year = {2014},
	booktitle = {Proceedings of the British Machine Vision Conference},
	publisher = {BMVA Press},
	editors = {Valstar, Michel and French, Andrew and Pridmore, Tony}
	doi = { }