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基于部位检测和子结构组合的行人检测方法 被引量：2

Pedestrian Detection Method Based on Part Detector and Substructure Assemble
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摘要 提出了一种基于部位检测和子结构组合的、可用于辅助驾驶或视频监控系统中行人检测的方法。首先使用头部分类器在整幅图像中检测,得到感兴趣区域;然后在每个感兴趣区域内使用头部、躯干、腿部以及左臂和右臂5个人体部位检测器分别检测并使用基于子结构的检测组合方法对部位检测结果进行组合,以得到最终结果。在不同数据库上的实验结果表明,本方法可以有效地用于移动或静止摄像机所拍摄的视频图像中的多姿态及部分遮挡的行人检测。 We presented a pedestrian detection method based on part detector and substructure assemble which can be used for driving assistant system or video surveillance system. Firstly we used the head classifier to search the whole image in order to get the ROI（Region of Interesting）. Then in each ROI five part detectors including head, torso, leg, left arm and right arm were used individually. Finally we verified the detection result through part detector assemble method based on substructure. Experiments on different database show that our method has high performance in detecting pedestrians with numerous poses and partial occlusion in cluttered background.
作者 胡斌 王生进 丁晓青
机构地区 智能技术与系统国家重点实验室清华信息科学与技术国家实验室清华大学电子工程系
出处 《计算机科学》 CSCD 北大核心 2009年第11期242-246,共5页 Computer Science
基金 863国家重点基金项目(2006AA01Z115) 973国家重点基金项目(2007CB311004)资助
关键词 视频图像 行人检测 部位检测器 子结构组合 Video images, Pedestrian detection, Part detector,Substructure assemble
分类号 TP24 [自动化与计算机技术—检测技术与自动化装置]
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参考文献20

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同被引文献15

  • 1Zegeer C V,Bushell M.Pedestrian Crash Trends and Potential Countermeasures from Around the World[J].Accident Analysis and Prevention,2012,44(1):3-11.
  • 2Dalal N,Triggs B.Histograms of Oriented Gradients for Human Detection[C].IEEE Conf.on Computer Vision and Pattern Recognition,San Diego,CA,USA:IEEE,2005,1:886-893.
  • 3Wu B,Nevatia R.Detection and Tracking of Multiple,Partially Occluded Humans by Bayesian Combination of Edgelet Based Part Detectors[J].Int.Journal of Computer Vision,2007,75(2):247-266.
  • 4Sabzmeydani P,Mori G.Detecting Pedestrians by Learning Shapelet Features[C].IEEE Conf.on Computer Vision and Pattern Recognition,Minneapolis,Minnesota,USA:IEEE,2007.
  • 5Zhang L,Wu B,Nevatia R.Pedestrian Detection in Infrared Images Based on Local Shape Features[C].IEEE Conf.Computer Vision and Pattern Recognition,Minneapolis,Minnesota,USA:IEEE,2007.
  • 6Dollar P,Wojek C,Schiele B,et al.Pedestrian Detection:An Evaluation of the State of the Art[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(4):743-761.
  • 7Enzweiler M,Gavrila D M.Monocular Pedestrian Detection:Survey and Experiments[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(12):2179-2195.
  • 8Paisitkriangkcrai S,Shen C H,Zhang J.Fast Pedestrian Detection Using a Cascade of Boosted Covariance Features[J].IEEE Transactions on Circuits and Systems for Video Technology,2008,18(8):1140-1151.
  • 9Laptev I.Improvements of Object Detection Using Boosted Histograms[J].Journal of Image and Vision Computing,2009,27:535-544.
  • 10Freund Y,Schapire E R.A Decision-Theoretic Generalization of on Line Learning and an Application to Boosting[C].Proceedings of the Second European Conference on Computational Learning Theory,Barcelona,Spain:Springer,1995:23-37.

引证文献2

二级引证文献4

计算机科学
2009年 第11期

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