Part Context Learning for Visual Tracking

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 图像与视频分析团队

	Part Context Learning for Visual Tracking
	Zhu, Guibo; Wang, Jinqiao; Zhao, Chaoyang; Lu, Hanqing; Jinqiao Wang
	2014
会议日期	September 1-5
会议地点	Nottingham, UK
关键词	Visual Tracking Part Context Learning
英文摘要	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 object itself to enhance the tracker’s 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 spatio 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 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 dataset, and the comparisons with state-of-the-arts demonstrate the effectiveness of our work.
会议录	In proceedings of British Machine Computer Vision
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/11758]
专题	自动化研究所_模式识别国家重点实验室_图像与视频分析团队
通讯作者	Jinqiao Wang
作者单位	Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Guibo,Wang, Jinqiao,Zhao, Chaoyang,et al. Part Context Learning for Visual Tracking[C]. 见:. Nottingham, UK. September 1-5.