Journal of System Simulation ›› 2016, Vol. 28 ›› Issue (9): 2186-2194.
Special Issue: 特约稿件
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Li Wei1,2, Wang Pengjie2, Song Haiyu2
Received:
2016-05-31
Revised:
2016-07-11
Online:
2016-09-08
Published:
2020-08-14
CLC Number:
Li Wei, Wang Pengjie, Song Haiyu. Survey on Pedestrian Detection Based on Statistical Classification[J]. Journal of System Simulation, 2016, 28(9): 2186-2194.
[1] | N Brewer, L Andersson, N Pettersson.A new pedestrian dataset for supervised learning[C]// IEEE Intelligent Vehicles Symp., USA: IEEE, 2008. |
[2] | C Papageorgiou, T Poggio.A trainable system for object detection[J]. Int. J. Comput.(S1573-1405), 2000, 38(1): 15-33. |
[3] | P Viola, M Jones.Rapid object detection using a boosted cascade of simple features[C]// Proc. IEEE Conf. Computer. Vision and Pattern Recognition. USA: IEEE, 2001 (1): 511-518. |
[4] | R Lienhart, J Maydt. An Extended Set of Haar-like Features for Rapid Object Detection [C]// International Conference on Image Processing. USA: IEEE, 2002, 1: I-900-I-903. |
[5] | G Overett, L Petersson, L Andersson, et al.Boosting a heterogeneous pool of fast HOG features for pedestrian and sign detection[C]// Intelligent Vehicles Symposium IEEE. USA: IEEE, 2009: 584-590. |
[6] | Gavrila D.Pedestrian detection from a moving vehicle[C]// Proceedings of European Conference Computer Vision. Dublin, Ireland: Lecture Notes in Computer Science, 2000: 37-49. |
[7] | Gavrila D, Giebel J, Munder S.Vision-based pedestrian detection: the protector system[C]// Intelligent Vehicles Symposium, Parma, Italy. USA: IEEE Computer Society, 2004: 13-18. |
[8] | Wu B, Nevatia R, Li Y.Segmentation of multiple, partially occluded objects by grouping, merging, assigning part detection responses[J]. Int’ l Journal of Computer Vision (S1573-1405), 2009(82): 185-204. |
[9] | Gao W, Ai H Z, Lao S.Adaptive contour features in oriented granular space for human detection and segmentation[C]// Proceedings of IEEE Conference Computer Vision and Pattern Recognition. Miami, Florida, USA. USA: IEEE Computer Society, 2009: 1786-1793. |
[10] | Sabzmeydani Payam. Mori, Greg Detecting Pedestrians by Learning Shapelet Features Computer Vision and Pattern Recognition (CVPR) [C]//2007 IEEE Conference on Minneapolis, MN (2007), USA. USA: IEEE, 2007. |
[11] | Yu LP, Yao WT, Liu HP, et al.A Monocular Vision Based Pedestrian Detection System for Intelligent Vehicles[C]// IEEE Intelligent Vehicles Symposium, USA: IEEE, 2008. |
[12] | N Dalal, B Triggs.Histograms of oriented gradients for human detection[C]// Proc. IEEE Int. Conf. CVPR. USA: IEEE, 2005: 886-893. |
[13] | Dollar P, Belongie S, Perona P.The fastest pedestrian detector in the west[C]// Proceedings of British Machine Vision Conference. Aberystwyth, UK : Lecture Notes in Computer Science, 2010: 1-11. |
[14] | Wojek C, Schiele B.A performance evaluation of single and multi-feature people detection[J]. Pattern Recognition (S0302-9743), 2008, 5096(1): 82-91. |
[15] | Wu J F, Yang S, Zhang L.Pedestrian detection based on improved HOG feature and robust adaptive boosting algorithm[C]// Image and Signal Processing, Shanghai, China. USA: IEEE Computer Society, 2011: 1535-1539. |
[16] | Zhu Q, Avidan S.Fast Human Detection Using a Cascade of Histograms of Oriented Gradients [C]// Anon. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA, 2006. New York, USA: IEEE Computer Society Press, 2006, 2: 1491-1498. |
[17] | Porikli F.Integral histogram: a fast way to extract histogram in cartesian spaces[C]// Proceedings of IEEE Conference Computer Vision and Pattern Recognition. San Diego, CA, USA: IEEE Computer Society, 2005: 829-836. |
[18] | S Walk, N Majer, K Schindler, et al.New features and insights for pedestrian detection[C]// IEEE Conf. Computer Vision and Pattern Recognition. USA: IEEE, 2010: 1030-1037. |
[19] | X Wang, T X Han, S Yan.A hog-lbp human detector with partial occlusion handling[C]// IEEE Intl. Conf. Computer Vision. USA: IEEE, 2009: 32-39. |
[20] | 车志富. 基于支持向量机的行人检测 [D]. 北京: 北京交通大学, 2010. |
[21] | 陶建峰. 基于多特征融合的行人检测方法研究 [D]. 南京: 南京理工大学, 2013. |
[22] | 王兴宝. 复杂场景下多姿态行人检测与识别方法研究[D]. 苏州: 苏州大学, 2012. |
[23] | Cutler R, Davis L S.Robust real-time periodic motion detection, analysis and applications[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (S0162-8828), 2000, 22(8): 781-796. |
[24] | Wohler C, Kressler U, Anlauf J K.Pedestrian recognition by classifcation of image sequences: global approaches vs. local spatio-temporal processing[C]// Proceedings of 15th International Conference on Pattern Recognition. Barcelona, Spain. USA: IEEE, 2000, 2: 540-544. |
[25] | Curio C, Edelbrunner J, Kalinke T, et al.Walking pedestrian recognition[J]. IEEE Transactions on Intelligent Transportation Systems (S1524-9050), 2000, 1(3): 155-163. |
[26] | Ran Yang, Zheng Qin-Fen, Weiss I, et al.Pedestrian classifcation from moving platforms using cyclic motion pattern[C]// Proceedings of International Conference on Image Processing, Genoa, Italy. USA: IEEE, 2005, 2: 854-857. |
[27] | Grubb G, Zelinsky A, Nilsson L, et al.3D vision sensing for improved pedestrian safety[C]// Proceedings of IEEE Intelligent Vehicles Symposium, Parma, Italy. USA: IEEE, 2004:19-24. |
[28] | Oren M, Papageorgiou C, Sinha P, et al.Pedestrian detection using wavelet templates[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico. USA: IEEE, 1997: 193-199. |
[29] | Maji S, Berg A, Malik J.Classification using intersection kernel SVMs is efficient[C]// Proceedings of IEEE Conference on Computer Vision and Patter Recognition, Anchorage, Alaska, USA. USA: IEEE Computer Society, 2008: 1-8. |
[30] | Felzenszwalb P, Girshick R B, McAllester D, et al. Object detection with discriminaltively trained part based models[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence (S0162-8828), 2009, 32(9): 1627-1645. |
[31] | Mohan A, Papageorgiou C, Poggio T.Example-based object detection in images by components[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (S0162-8828), 2001, 23(4): 349-361. |
[32] | Cheng Hong, Zheng Nanning, Qin Junjie.Pedestrian detection using sparse Gabor filter and support vector machine[C]// Proceedings of IEEE Intelligent Vehicles Symposium, Vienna, Austria. USA: IEEE, 2005: 583. |
[33] | Dai Congxia, Zheng Yunfei, Li Xin.Layered representation for pedestrian detection and tracking in infrared imagery[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA. USA: IEEE, 2005, 3: 13. |
[34] | Grubb G, Zelinsky A, Nilsson L, et al.3D vision sensing for improved pedestrian safety[C]// Proceedings of IEEE Intelligent Vehicles Symposium, Parma, Italy. USA: IEEE, 2004: 19-24. |
[35] | Zhao Liang, Thorpe C.Stereo and neural network-based pedestrian detection[J]. IEEE Transactions on Intelligent Transportation Systems (S1524-9050), 2000, 1(3): 148-154. |
[36] | Franke U, Joos A.Real-time stereo vision for urban traffic scene understanding[C]// Proceedings of IEEE Intelligent Vehicles Symposium, Dearborn, MI, USA. USA: IEEE, 2000: 273-278. |
[37] | Szaras M, Yoshizawa A, Yamamoto M, et al.Pedestrian detection with convolutional neural networks[C]// Proceedings of IEEE Intelligent Vehicles Symposium. Las Vegas, Nevada, USA. USA: IEEE, 2005: 224-229. |
[38] | Freund BY, Schapire RE.A Decision-theoretic Generalization of Online Learning and an Application to Boosting[J]. Journal of Computer and System Sciences,(S0022-0000), 1997, 55(1): 119-139. |
[39] | Friedman J.Greedy function approximation: a gradient boosting machine[J]. Annals of Statistics,(S0090-5364), 2001, 29(5): 1189-1232. |
[40] | Paul viola, Michael Jones. Robust Real-time Object Detection[J]. International Journal of Computer Vision,(S0920-5691), 2001, 57(2): 87-112. |
[41] | Abramson Y, Steux B.Hardware-friendly pedestrian detection and impact prediction[C]// Proceedings of IEEE Intelligent Vehicles Symposium. Parma, Italy. USA: IEEE, 2004: 590-595. |
[42] | Shashua A, Gdalyahu Y, Hayun G.Pedestrian detection for driving assistance systems: single-frame classification and system level performance[C]// Proceedings of IEEE Intelligent Vehicles Symposium, Parma, Italy. USA: IEEE, 2004: 1-6. |
[43] | K Mikolajczyk, Schmid C, Zisserman A.2004. Human detection based on a probabilistic assembly robust part detectors[C]// Proceeding of European Conference on Computer Vision. Prague, Czech Republic: Springer: 69-82. |
[44] | Oren M, Papageorgiou C, Sinha P'Osuna E, et al. Pedestrian detection using wavelet templates[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. San Juan, Puerto Rico. USA: IEEE, 1997: 193-199. |
[45] | 王根岭. 行人检测与跟踪中若干关键技术的研究 [D].杭州: 浙江理工大学, 2014. |
[46] | 石娟峰. 基于视频的行人检测和跟踪 [D]. 北京: 北京邮电大学, 2012. |
[47] | 周千昊. 自然背景下的行人检测 [D]. 上海: 上海交通大学, 2009. |
[48] | 徐翠. 基于计算机视觉的汽车安全辅助驾驶若干关键问题研究 [D]. 合肥: 中国科学技术大学, 2009. |
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