Trajectory Learning and Analysis Based on Kernel Density Estimation

doi:10.1109/ITSC.2009.5309677

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	Trajectory Learning and Analysis Based on Kernel Density Estimation
	Zhou, Jianying 1; Wang, Kunfeng(王坤峰)1 ; Tang, Shuming1,2 ; Wang, Fei-Yue1,3
	2009
会议名称	12th IEEE International Conference on Intelligent Transportation Systems
会议录名称	IEEE International Conference on Intelligent Transportation Systems (ITSC)
卷号	2009
页码	178-183
会议日期	2009
会议地点	St. Louis, MO, USA
出版地	IEEE
摘要	This paper presents a novel kernel density estimation approach to vehicle trajectory learning and motion analysis. The framework comprises a training stage and a testing stage. In the training stage, vehicle trajectories are first clustered by the hierarchical spectral clustering method. Then, through the proposed kernel density estimation approach, the average kernel density of one point on a trajectory can be estimated. In the testing stage, the compactness estimated by a Gaussian kernel function is introduced. Abnormal trajectories are detected with compactness lower than expected for a few consecutive frames. Vehicle motions are identified into multiple activities with their respective trajectory compactness.
DOI	10.1109/ITSC.2009.5309677
收录类别	EI
引用统计
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/5044
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
作者单位	1.Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Science, Beijing 100190, China 2.Shandong University of Science and Technology, Qingdao 266510, China 3.University of Arizona, Tucson, AZ 85721 USA
推荐引用方式 GB/T 7714	Zhou, Jianying,Wang, Kunfeng,Tang, Shuming,et al. Trajectory Learning and Analysis Based on Kernel Density Estimation[C]. IEEE,2009:178-183.

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