3D Model Based Vehicle Localization by Optimizing Local Gradient Based Fitness Evaluation

CASIA OpenIR > 模式识别实验室

	3D Model Based Vehicle Localization by Optimizing Local Gradient Based Fitness Evaluation
	Zhaoxiang Zhang; Min Li; Kaiqi Huang; Tieniu Tan
	2009-12-08
会议名称	19th International Conference on Pattern Recognition
会议录名称	Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
页码	1-4
会议日期	8-11 December 2009
会议地点	Tampa, Florida, USA
摘要	We address the problem of 3D model based vehicle localization in calibrated traffic scenes. A wire-frame vehicle model is set up as prior information and an efficient local gradient based method is proposed to evaluate the fitness between the projection of 3D model and image data, which illustrates smooth optimization surface and more conspicuous peak with low computational cost. Gradient decent is then applied to optimize the evaluation score for localization. Experimental results demonstrate the accuracy, efficiency and robustness of the proposed method for model based vehicle localization.
关键词	Vehicles Optimization Methods Data Mining Noise Robustness Pixel Laboratories Pattern Recognition Automation Traffic Control Layout
语种	英语
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12713
专题	模式识别实验室
通讯作者	Zhaoxiang Zhang
推荐引用方式 GB/T 7714	Zhaoxiang Zhang,Min Li,Kaiqi Huang,et al. 3D Model Based Vehicle Localization by Optimizing Local Gradient Based Fitness Evaluation[C],2009:1-4.