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3D Model Based Vehicle Localization by Optimizing Local Gradient Based Fitness Evaluation
Zhaoxiang Zhang; Min Li; Kaiqi Huang; Tieniu Tan
2009-12-08
Conference Name19th International Conference on Pattern Recognition
Source Publication Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Pages1-4
Conference Date8-11 December 2009
Conference PlaceTampa, Florida, USA
AbstractWe 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.
KeywordVehicles Optimization Methods Data Mining Noise Robustness Pixel Laboratories Pattern Recognition Automation Traffic Control Layout
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12713
Collection智能感知与计算研究中心
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
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.
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