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双摄像机下人体遮挡时的跟踪方法 被引量:1

Human tracking method under occlusion with two cameras
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摘要 针对双摄像机下存在人体遮挡情况时的跟踪问题,提出了利用人体3维位置信息来实现跟踪的方法。该方法首先对其中一个摄像机视频图像中的人体像素抽样,接着在其他摄像机视频图像中找出抽样像素的匹配点,计算出每一对匹配点在世界坐标系中所对应的3维点,然后依据3维位置信息将3维点聚类,找出每一个聚类区域中的3维点所对应的图像中的一组像素点,并对其构建高斯平滑直方图模型。在此基础上,依据直方图模型将互相遮挡的人体分割开来,最后根据求取的人体像素点的匹配关系来确定不同摄像机中同一个人的对应关系。实验结果表明,该方法能有效实现遮挡情况下的人体跟踪。 The cameras in this study are used to extend view field. The paper proposed a three-dimensional information based human tracking method. The method firstly samples the pixels within the human body in one of the video images, and then finds the matching points in the other simultaneous video images by polar line constraints. The three-dimensional points in space corresponding to each pair of the matches are obtained by triangulation and are also clustered. The pixels in the image plane corresponding to each clustering region which includes the three-dimensional points are decided. Finally, Gaussian-smoothing histograms are created based on the pixels in the image plane. Occluded people would be divided into each other by the Gaussian-smoothing histograms. The matching relation of each person in different cameras could be found and thus the human tracking is realized. Experimental results show the efficiency of the method.
作者 张莉 于海滨
出处 《中国图象图形学报》 CSCD 北大核心 2011年第4期606-612,共7页 Journal of Image and Graphics
基金 浙江省自然科学基金项目(Y1090881)
关键词 人体跟踪 视频监控 遮挡 高斯平滑直方图 human tracking video surveillance occlusion Gaussian-smoothing histograms
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  • 1Tao H,Sawhney H S,Kumar R.Object tracking with Bayesian estimation of dynamic layer representations[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(1):75-89. 被引量:1
  • 2Ristivojevic M,Konrad J.Space-time image sequence analysis:object tunnels and occlusion volumes[J].IEEE Transactions on Image Processing,2006,15(2):364-376. 被引量:1
  • 3Zhao T,Nevatia R,Wu B.Segmentation and tracking of multiple humans in crowded environments[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,3(7):1198-1211. 被引量:1
  • 4Wang Xin,Liu Lei,Tang Zhenmin.Infrared human tracking with improved mean shift algorithm based on multicue fusion[J].Appl.Opt.,2009,(48):4201-4212. 被引量:1
  • 5Fleuret F,Berclaz J,Lengagne R,et al.Multi camera people tracking with a probabilistic occupancy map[J].IEEE Transactions Pattern Anal.Mach.Intell.,2007,30 (2):267-282. 被引量:1
  • 6Yu Y,Harwood D,Yoon K,et al.Human appearance modeling for matching across video sequences[J].Mach.Vis.Appl.,2007,18(3-4):139-149. 被引量:1
  • 7张泽旭,李金宗,李冬冬.一种运动目标多特征点的鲁棒跟踪方法研究[J].数据采集与处理,2003,18(4):423-428. 被引量:6
  • 8Utsumi A,Mori H,Ohya J,et al.Multiple-view-based tracking of multiple humans[C]//Proceedings of the 14th International Conference on Pattern Recognition.Washington,DC:IEEE Computer Society,1998:597-601. 被引量:1
  • 9Shi J,Tomashi C.Good feature to track[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Ithaca,USA,Cornell University,1994:593-600. 被引量:1
  • 10Lowe D.Distinctive image features from scale-invariatu keypoints[J].International Journal of Computer Vision,2004,60 (2):91-110. 被引量:1

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