
Pedestrian Detection Combined with Single and Couple Pedestrian DPM Models in Traffic Scene
ZENG Jie-xian, CHEN Xiao
ACTA ELECTRONICA SINICA ›› 2016, Vol. 44 ›› Issue (11) : 2668-2675.
Pedestrian Detection Combined with Single and Couple Pedestrian DPM Models in Traffic Scene
In this paper,a new kind of pedestrian detection method is investigated;the single DMP model is combined with the couple pedestrian DPM to solve the pedestrian detection problem because of the pedestrian visual occlusion under common traffic models.This method extracts DPM feature through dataset such as INRIA,ETH,and then obtains the single/couple DPM model through LatentSVM training method.Moreover,the traffic pedestrian.distribution scene can be classified and divided first and then separated and remixed by the classification detention method.Firstly the target image will match with couple pedestrian template SDP-DPM.Secondly if couple pedestrian target can not be detected,the scene will be classified as single distribution,and then matching template will switch to single pedestrian template,the results will be saved.Thirdly when the couple pedestrian are detected,the distribution will be classified and mixed,and then corresponding couple pedestrian filtering response can be saved.Finally the second matching will launch with single pedestrian template,weighted sum of the two detection results.The test proved that the method stated above can efficiently detect pedestrians under scenes that pedestrian heavily cover each other,and this method also can be more accurately compared with the traditional DPM method and other popular detection methods.
pedestrian detection / DPM / occlusion / traffic scene {{custom_keyword}} /
[1] 苏松志,李绍滋,陈淑媛,等.行人检测技术综述[J].电子学报,2012,40(4):814-820. Su Song-zhi,Li Shao-zi,Chen Shu-yuan,et al.A survey on pedestrian detection[J].Acta Electronica Sinica,2012,40(4):814-820.(in Chinese)
[2] Viola P,Jones M J.Robust real-time face detection[J].IEEE Transactions on International Journal of Computer Vision,2004,57(2):137-154.
[3] Yuanyuan D,Jing X.Contextual boost for pedestrian detection[A].IEEE Conference on Computer Vision and Pattern Recognition[C].Rhode Island,American:IEEE,2012.2895-2902.
[4] B Wu,R Nevatia.Detection of multiple,partially occluded humans in a single image by bayesian combination of edgelet part detectors[A].IEEE Conference on Computer Vision and Pattern Recognition[C].Beijing,China:IEEE,2005.90-97.
[5] N Dalal,B.Triggs.Histograms of oriented gradients for human detection[A].IEEE Conference on Computer Vision and Pattern Recognition[C].San Diego,USA:IEEE 2005.886-893
[6] Ningbo Wang,Xiaojin Gong,Jilin Liu.A new depth descriptor for pedestrian detection in RGB-D images[A].IEEE Conference on International Conference on Pattern Recognition[C].Tsukuba,Japan:IEEE,2012.3688-3691.
[7] Dollar 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,2012,99:1-20.
[8] Xiaoyu W,Han T X,Shuicheng Y.An HOG-LBP human detector with partial occlusion handling[A].IEEE Conference on International Conference on Computer Vision[C].Kyoto,JAPAN:IEEE,2009:32-39.
[9] Viola P,Jones M J,Snow D.Detecting pedestrians using patterns of motion and appearance[J].IEEE Transactions on International Journal of Computer Vision,2005,63(2):153-161.
[10] S Walk,N Majer,K Schindler,et al.New features and insights for pedestrian detection[A].IEEE Conference on Computer Vision and Pattern Recognition[C].San Francisco,USA:IEEE,2010.1030-1037.
[11] 刘威,段成伟,遇冰,等.基于后验HOG特征的多姿态行人检测[J].电子学报,2015,43(2):217-224. Liu Wei,Duan Cheng-wei,Yu Bing,et al.Method research on vehicular infrared pedestrian detection based on local features[J].Acta Electronica Sinica,2015,43(2):217-224.(in Chinese)
[12] Wojek C,Schiele B.A Performance Evaluation of Single and Multi-Feature People Detection[M].Pattern Recognition.Springer Berlin Heidelberg,2008:82-91.
[13] Zhu Q,Yeh M C,Cheng K T,et al.Fast human detection using a cascade of histograms of oriented gradients[A].IEEE Conference on International Conference on Computer Vision[C].New York,American:IEEE,2006.1491-1498.
[14] 田广,戚飞虎.移动摄像机环境下基于特征变换和SVM的分级行人检测算法[J].电子学报,2008,36(5):1024-1028. TIAN Guang,QI Fei-hu.Feature transformation and SVM based hierarchical pedestrian detection with a monocular moving camera[J].Acta Electronica Sinica,2008,36(5):1024-1028.(in Chinese)
[15] 王国华,刘琼,庄家俊.基于局部特征的车载红外行人检测方法研究[J].电子学报,2015,43(7):1444-1448. WANG Guo-hua,LIU Qiong,ZHUANG Jia-jun.Method research on vehicular infrared pedestrian detection based on local features[J].Acta Electronica Sinica,2015,43(7):1444-1448.(in Chinese)
[16] S Paisitkriangkrai,C Shen,A van den Hengel.Efficient pedestrian detection by directly optimize the partial area under the roc curve[A].IEEE Conference on International Conference on Computer Vision[C].Sydney,Australia:IEEE,2013.1057-1064.
[17] W R Schwartz,A Kembhavi,D Harwood,et al.Human detection using partial least squares analysis[A].IEEE Conference on International Conference on Computer Vision[C].Kyoto,Japan:IEEE,2009.24-31.
[18] W Ouyang,Wang X Joint deep learning for pedestrian detection[A].IEEE Conference on Computer Vision and Pattern Recognition[C].Portland,USA:IEEE,2013.2056-2063.
[19] Hosang J,Omran M,Benenson R.Taking a deeper look at pedestrians[A].IEEE Conference on Computer Vision and Pattern Recognition[C].Boston,USA:IEEE,2015.4073-4082.
[20] P.Felzenszwalb,R.B.Grishick,D.McAllister,et al.Object detection with discriminatively trained partbased models[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2010,32(9):1627-1645.
[21] Meng W,Wei L,Xiaogang W.Transferring a generic pedestrian detector towards specific scenes[A].IEEE Conference on Computer Vision and Pattern Recognition[C].Rhode Island,American:IEEE,2012.3274-3281.
[22] Tang S,Andriluka M,Schiele B.Detection and tracking of occluded people[J].IEEE Transactions on International Journal of Computer Vision,2014,110(1):58-69.
[23] Benenson R,Omran M,Hosang J,et al.Ten years of pedestrian detection,what have we learned[A].IEEE Conference on European Conference on Computer Vision[C].Columbus,USA:IEEE,2014.613-627.
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