面向道路安全的行人检测研究
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摘要
行人安全作为智能交通系统中的重要一环,越来越受到各个国家的重视,而对他的检测则是确保其安全的首要目标。目前的行人检测算法中,普遍采用单个传感器的方法,比如单目视觉和激光雷达。本论文以智能车为平台,采用摄像头和激光雷达融合的方法实现行人检测,并对多车协作实现行人检测展开了一定的研究。
本文一共分为四个部分。第一个部分主要介绍了摄像头传感器与激光雷达传感器之间联合标定的过程以及这两种传感器同步的方法,它是实现兴趣区域提取的关键步骤,通过两种传感器之间的协作降低算法复杂度。第二部分介绍了行人检测的主要算法,通过联合标定确定的候选区域,通过图像特征和基于Hausdorff距离的模板匹配方法定位行人。第三部分提出了一种基于多车协作来实现行人检测的方法,它可以实现车辆在被遮挡情况下仍能检测到行人并进行预警。本文分别采用GPS和道路信标两种方法来实现。第四部分主要介绍了对道路周边环境的辅助检测,包括停车线检测和道路交通标志检测,它对于进一步确保行人安全有一定的实际意义。
More and more countries pay attention to pedestrian safety for its importance in intelligent traffic system. Then, pedestrian detection is the main target in order to ensure their safety. The universal algorithm for pedestrian detection is based on single sensor such as monocular or laser scanner. This paper introduces a multi-sensor fusion method to realize pedestrian detection based on camera and laser scanner which are installed in intelligent vehicle. This paper also has a research on multi-car cooperation to realize pedestrian detection.
This paper is divided into four sections. The first part introduces a method of the co-calibration and synchronization between a camera and laser scanner. A proper ROI(region of interests) can be located based on this method which can reduce the algorithm complexity. The second part introduces the main algorithm of pedestrian detection. Through the ROI induced from co-calibration, a method for pedestrian detection is introduced based on image characteristics and a template matching method of Hausdorff distance. The third part presents a pedestrian detection way based on the cooperation between car and car. It can realize pedestrian detection when the car is occluded and warn the pedestrian. This paper uses GPS and traffic beacon to carry out this method. The fourth part introduces the additional detection for road environment。It includes stop line and traffic sign detection. It is helpful for pedestrian safety.
本文一共分为四个部分。第一个部分主要介绍了摄像头传感器与激光雷达传感器之间联合标定的过程以及这两种传感器同步的方法,它是实现兴趣区域提取的关键步骤,通过两种传感器之间的协作降低算法复杂度。第二部分介绍了行人检测的主要算法,通过联合标定确定的候选区域,通过图像特征和基于Hausdorff距离的模板匹配方法定位行人。第三部分提出了一种基于多车协作来实现行人检测的方法,它可以实现车辆在被遮挡情况下仍能检测到行人并进行预警。本文分别采用GPS和道路信标两种方法来实现。第四部分主要介绍了对道路周边环境的辅助检测,包括停车线检测和道路交通标志检测,它对于进一步确保行人安全有一定的实际意义。
More and more countries pay attention to pedestrian safety for its importance in intelligent traffic system. Then, pedestrian detection is the main target in order to ensure their safety. The universal algorithm for pedestrian detection is based on single sensor such as monocular or laser scanner. This paper introduces a multi-sensor fusion method to realize pedestrian detection based on camera and laser scanner which are installed in intelligent vehicle. This paper also has a research on multi-car cooperation to realize pedestrian detection.
This paper is divided into four sections. The first part introduces a method of the co-calibration and synchronization between a camera and laser scanner. A proper ROI(region of interests) can be located based on this method which can reduce the algorithm complexity. The second part introduces the main algorithm of pedestrian detection. Through the ROI induced from co-calibration, a method for pedestrian detection is introduced based on image characteristics and a template matching method of Hausdorff distance. The third part presents a pedestrian detection way based on the cooperation between car and car. It can realize pedestrian detection when the car is occluded and warn the pedestrian. This paper uses GPS and traffic beacon to carry out this method. The fourth part introduces the additional detection for road environment。It includes stop line and traffic sign detection. It is helpful for pedestrian safety.
引文
[1] G.Sala. Accident Analysis. PROTECTOR Deliverable, 2000.
[2]刘强,陆化普等.我国道路交通事故特征分析与对策研究.中国安全科学学报, 2006, (6): 123-128.
[3]蔡果,刘江鸿,杨降勇等.城市道路交通中行人安全问题研究.华北科技学院学报, 2005, 2(4): 60-65.
[4] D.M.Gavrila, J.Giebel, S.Munde. Vision-based Pedestrian Detection: the PROTECTOR System. Parma, Italy: Proceedings of IEEE International Conference on Intelligent Vehicles Symposium, 2004:13-18.
[5] D.M.Gavrila, M.Kunert and U.Lages. A Multi-sensor Approach for the Protection of Vulnerable Traffic Participants - the PROTECTOR project. Budapest, Hungary: Proc. of the IEEE Instrumentation and Measurement Technology Conference, Vol.3, 2001: 2044-2048.
[6] http://www.save-u.org
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[9] The Chamfer System. http://www.gavrila.net
[10] http://www.daimlerchrysler.com
[11] D.M.Gavrila and J. Giebel. Shape-based Pedestrian Detection and Tracking. Paris, France: Proc. of the IEEE Intelligent Vehicles Symposium, 2002: 8-14.
[12] D.M.Gavrila. Sensor-based Pedestrian Protection. IEEE Intelligent Systems, Vol.16, No.6, 2001: 77- 81.
[13] D.M.Gavrila. Pedestrian Detection from a Moving Vehicle. Dublin, Ireland: Proc. of European Conference on Computer Vision, 2000: 37-49.
[14] M.Bertozzi, A.Broggi, R.Chapuis, F.Chausse etc. Shape-based Pedestrian Detection and Localization. Ohio, USA: Procs IEEE Intelligent Vehicles Symposium, June 2003: 410-415.
[15] L.Zhao, C.Thorpe. Stereo and Neural Network-based Pedestrian Detection. IEEE Transactions on Intelligent Transportation Systems, September, 2000, 1 (3): 148-154.
[16] H.Nanda, L.Davis. Probabilistic Template Based Pedestrian Detection in InfraredVideos. Paris, France: Proc. IEEE Intelligent Vehicles Symposium, June 2002: 15-20.
[17] Y.Fang, K.Yamada, Y.Ninomiya, and etc. A Shape-Independent-Method for Pedestrian Detection with Far-Infrared-Images. IEEE Transactions on Vehicular Technology, Vol.53, No.6, Nov. 2004: 1679- 1697.
[18] K.C.Fuerstenberg, K.C.J.Dietmayer, V.Willhoeft. Pedestrian Recognition in Urban Traffic Using a Vehicle based Multilayer Laserscanner. Versailles, France: Proceedings of IEEE Intelligent Vehicle Symposium, June 2002:31-35.
[19] M.Tons, R.Doerfler, M.M.Meinecke, M.A.Obojski. Radar Sensors and Sensor Platform Used for Pedestrian Protection in the EC-funded Project SAVE-U. Parma, Italy: Proceedings of IEEE Intelligent Vehicles Symposium, 2004:813-818.
[20] Sugimoto Mikio, Aoyama Kenichi, Shimizu Daisuke, Maekawa Yasuaki. Realization of Head-on Collision Warning System at Intersections - DSSS: Driving Safety Support Systems. Dearbon, USA : IEEE Intelligent Vehicles Symposium, 2000:731-735.
[21] T.Kato, Y.Ninomiya, I.Masaki. An Obstacle Detection Method by Fusion of Radar and Motion Stereo. IEEE Transactions on Intelligent Transportation Systems, 2002,:182-188.
[22] H.G.Jung, Y.H.Lee, P.J.Yoon, I.Y.Hwang, J.Kim. Sensor Fusion based Obstacle Detection/ Classification for Active Pedestrian Protection System. Lake Tahoe,USA: Proceedings of Advances in Visual Computing, 2006:294-305.
[23] J.Zhao, D.Y.Yao. Occlusion Adaptive Object Tracking based on Video Image and Radar Data. Chengdu, China: Proceedings of IEEE International Conference on ITS Telecommunications, 2006: 943-946.
[24] Axel Sikora. Communication and Localization for a Cooperative eSafety-System. Dortmund, Germany: IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems, September 2007: 682-685.
[25] J.A.Misener1, S.E.Shladover. PATH Investigations in Vehicle-Roadside Cooperation and Safety: A Foundation for Safety and Vehicle-Infrastructure Integration Research. Toronto, Canada: Proceedings of the IEEE Intelligent Transportation Systems Conference, September 17-20, 2006:9-16.
[26] http://www.toyota.co.jp/en/news/06/1130.html.
[27]贾慧星,章毓晋.车辆辅助驾驶系统中基于计算机视觉的行人检测研究综述.自动化学报, 2007, Vol.33, No.1:84-90.
[28]胡斌,何克忠.计算机视觉在室外移动机器人中的应用.自动化学报, 2006, Vol.32, No.5: 775-784.
[29]郭烈,王荣本,顾剥园,余天洪.世界智能车辆行人检测技术综述.公路交通科技, 2005,Vol.22, No.11:133-137.
[30]郭烈.基于单目视觉的车辆前方行人检测技术研究.吉林大学博士论文,2007.
[31]田广.基于视觉的行人检测与跟踪.上海交通大学博士论文,2007.
[32]许言午,曹先彬,乔红.行人检测系统研究新进展及关键技术展望,电子学报, 2008,Vol.36, No.5:962-968.
[33]李斌,史忠科.基于计算机视觉的行人检测技术的发展.计算机工程与设计, 2005, 26(10):2565-2568.
[34]常好丽,史忠科.基于单目视觉的运动行人检测与跟踪方法.交通运输工程学报,2006,6(2): 55-59.
[35]马奔.图象检测与行为理解研究.西北工业大学博士论文,2006.
[36]黄琛.基于Hausdorff距离模板匹配的行人检测算法研究与应用[D].硕士学位论文,清华大学,2006.5.
[37]余燕基于红外CCD与激光测距仪融合的行人检测技术研究吉林大学硕士学位论文,2008.
[38] Bo Wu, Ram Nevatia, Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors, International Journal of Computer Vision, volume 75, Issue 2(November 2007), pages 247~266 ISSN:0920-5691
[39] Shashua A, Gdalyahu Y, Hayun G, Pedestrian detection for driving assistance systems: single-frame classification and system level performance. In: Proceedings of IEEE Intelligent Vehicles Symposium, Parma, Italy, IEEE, 2004, 1~6
[40] Gavrila. D M, Pedestrian detection from a moving vehicle. In: Proceedings of the 6th European Conference on Computer Vision-Part II. London, UK: Springer-Verlag, 2000. 37~49
[41]李颢,杨明.基于非线性逆透视变换的摄像机畸变参数标定.上海交通大学学报. 2008, 42(10):1736-1739
[42]刘大学,戴斌,李政,贺汉根.一种单线激光雷达和可见光摄像机的标定方法[J].华中科技大学学报:自然科学版.2008, 36(S1): 68-71.
[43]马颂德,张正友.计算机视觉——计算理论与算法基础[M].北京:科学出版社. 1998
[44] von der Hardt, H.-J.; Wolf, D.; Husson, R.; The dead reckoning localization system of the wheeled mobile robot ROMANE. In: Multisensor Fusion and Integration for Intelligent Systems, 1996. IEEE/SICE/RSJ International Conference on8-11 Dec. 1996 Page(s):603 - 610
[45] Abel Mendel, Urbano Nunes, Situation-based Multi-target Detection and Tracking with Laser-scanner in Outdoor Semi-structured Environment, Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, September 28– October 2, 2004, Sendai, Japan, 2004, pp. 88-93
[46] Sérgio Santos, JoséEduardo Faria, Fernando Soares, Rui Araújo and Urbano Nunes, Tracking of Multi-Obstacles with Laser Range Data For Autonomous Vehicle, ROBOTICA2003, May, 2003
[47]罗军辉,冯平,哈力旦.A. MATLAB7.0在图像处理中的应用[M].北京:机械工业出版社,2005.6
[48]刘瑞祯,于仕琪. OpenCV教程.基础篇[M].北京:北京航空航天大学出版社,2007.6
[49] J.M collado, C.Hilario, A.de la Escalera etc. Model based vehicle detection for intelligent vehicles, Parma, Italy: Procs IEEE Intelligent Vehicles Symposium, 2004:572-577
[50] Huachun Tan, Yu-Jin Zhang. Computing Eigenface from Edge Images for Face Recognition Based on Hausdorff Distance. Image and Graphics, 2007, ICIG 2007, Fourth International Conference on 22-24 Aug.2007 Pages: 639-644
[51] Nordsjo, A.E. Target tracking based on Kalman-type filters combined with recursive estimation of model disturbances; Radar Conference, 2005 IEEE International9-12 May 2005 Page(s):115– 120
[52]王惠南,GPS原理与应用,北京:科学出版社,2003年8月
[53]徐绍铨,张华海,杨志强,王泽民,GPS测量原理及应用,武汉大学出版社,2002年
[54]董锐,车辆监控系统中的坐标转换及数据库应用,哈尔滨工程大学,2003年
[55]李征航,黄劲松,GPS测量与数据处理,武汉大学出版社,2005年
[56] NavCom Technology Inc, NCT-2100D Datasheet, 05051-71, 200
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[2]刘强,陆化普等.我国道路交通事故特征分析与对策研究.中国安全科学学报, 2006, (6): 123-128.
[3]蔡果,刘江鸿,杨降勇等.城市道路交通中行人安全问题研究.华北科技学院学报, 2005, 2(4): 60-65.
[4] D.M.Gavrila, J.Giebel, S.Munde. Vision-based Pedestrian Detection: the PROTECTOR System. Parma, Italy: Proceedings of IEEE International Conference on Intelligent Vehicles Symposium, 2004:13-18.
[5] D.M.Gavrila, M.Kunert and U.Lages. A Multi-sensor Approach for the Protection of Vulnerable Traffic Participants - the PROTECTOR project. Budapest, Hungary: Proc. of the IEEE Instrumentation and Measurement Technology Conference, Vol.3, 2001: 2044-2048.
[6] http://www.save-u.org
[7] P.Marchal. SAVE-U: Sensors and System Architecture for Vulnerable Road Users Protection. Bruxelles: ADASE II-3rd Concertation meeting, Jan, 2004: 19-20.
[8] P.Marchal, D.Gavrila, L.Letellier, et al. SAVE-U: An innovative sensor platform for Vulnerable Road User protection[C]. Intelligent Transport Systems and Services, Madrid, Spain, November 2003.
[9] The Chamfer System. http://www.gavrila.net
[10] http://www.daimlerchrysler.com
[11] D.M.Gavrila and J. Giebel. Shape-based Pedestrian Detection and Tracking. Paris, France: Proc. of the IEEE Intelligent Vehicles Symposium, 2002: 8-14.
[12] D.M.Gavrila. Sensor-based Pedestrian Protection. IEEE Intelligent Systems, Vol.16, No.6, 2001: 77- 81.
[13] D.M.Gavrila. Pedestrian Detection from a Moving Vehicle. Dublin, Ireland: Proc. of European Conference on Computer Vision, 2000: 37-49.
[14] M.Bertozzi, A.Broggi, R.Chapuis, F.Chausse etc. Shape-based Pedestrian Detection and Localization. Ohio, USA: Procs IEEE Intelligent Vehicles Symposium, June 2003: 410-415.
[15] L.Zhao, C.Thorpe. Stereo and Neural Network-based Pedestrian Detection. IEEE Transactions on Intelligent Transportation Systems, September, 2000, 1 (3): 148-154.
[16] H.Nanda, L.Davis. Probabilistic Template Based Pedestrian Detection in InfraredVideos. Paris, France: Proc. IEEE Intelligent Vehicles Symposium, June 2002: 15-20.
[17] Y.Fang, K.Yamada, Y.Ninomiya, and etc. A Shape-Independent-Method for Pedestrian Detection with Far-Infrared-Images. IEEE Transactions on Vehicular Technology, Vol.53, No.6, Nov. 2004: 1679- 1697.
[18] K.C.Fuerstenberg, K.C.J.Dietmayer, V.Willhoeft. Pedestrian Recognition in Urban Traffic Using a Vehicle based Multilayer Laserscanner. Versailles, France: Proceedings of IEEE Intelligent Vehicle Symposium, June 2002:31-35.
[19] M.Tons, R.Doerfler, M.M.Meinecke, M.A.Obojski. Radar Sensors and Sensor Platform Used for Pedestrian Protection in the EC-funded Project SAVE-U. Parma, Italy: Proceedings of IEEE Intelligent Vehicles Symposium, 2004:813-818.
[20] Sugimoto Mikio, Aoyama Kenichi, Shimizu Daisuke, Maekawa Yasuaki. Realization of Head-on Collision Warning System at Intersections - DSSS: Driving Safety Support Systems. Dearbon, USA : IEEE Intelligent Vehicles Symposium, 2000:731-735.
[21] T.Kato, Y.Ninomiya, I.Masaki. An Obstacle Detection Method by Fusion of Radar and Motion Stereo. IEEE Transactions on Intelligent Transportation Systems, 2002,:182-188.
[22] H.G.Jung, Y.H.Lee, P.J.Yoon, I.Y.Hwang, J.Kim. Sensor Fusion based Obstacle Detection/ Classification for Active Pedestrian Protection System. Lake Tahoe,USA: Proceedings of Advances in Visual Computing, 2006:294-305.
[23] J.Zhao, D.Y.Yao. Occlusion Adaptive Object Tracking based on Video Image and Radar Data. Chengdu, China: Proceedings of IEEE International Conference on ITS Telecommunications, 2006: 943-946.
[24] Axel Sikora. Communication and Localization for a Cooperative eSafety-System. Dortmund, Germany: IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems, September 2007: 682-685.
[25] J.A.Misener1, S.E.Shladover. PATH Investigations in Vehicle-Roadside Cooperation and Safety: A Foundation for Safety and Vehicle-Infrastructure Integration Research. Toronto, Canada: Proceedings of the IEEE Intelligent Transportation Systems Conference, September 17-20, 2006:9-16.
[26] http://www.toyota.co.jp/en/news/06/1130.html.
[27]贾慧星,章毓晋.车辆辅助驾驶系统中基于计算机视觉的行人检测研究综述.自动化学报, 2007, Vol.33, No.1:84-90.
[28]胡斌,何克忠.计算机视觉在室外移动机器人中的应用.自动化学报, 2006, Vol.32, No.5: 775-784.
[29]郭烈,王荣本,顾剥园,余天洪.世界智能车辆行人检测技术综述.公路交通科技, 2005,Vol.22, No.11:133-137.
[30]郭烈.基于单目视觉的车辆前方行人检测技术研究.吉林大学博士论文,2007.
[31]田广.基于视觉的行人检测与跟踪.上海交通大学博士论文,2007.
[32]许言午,曹先彬,乔红.行人检测系统研究新进展及关键技术展望,电子学报, 2008,Vol.36, No.5:962-968.
[33]李斌,史忠科.基于计算机视觉的行人检测技术的发展.计算机工程与设计, 2005, 26(10):2565-2568.
[34]常好丽,史忠科.基于单目视觉的运动行人检测与跟踪方法.交通运输工程学报,2006,6(2): 55-59.
[35]马奔.图象检测与行为理解研究.西北工业大学博士论文,2006.
[36]黄琛.基于Hausdorff距离模板匹配的行人检测算法研究与应用[D].硕士学位论文,清华大学,2006.5.
[37]余燕基于红外CCD与激光测距仪融合的行人检测技术研究吉林大学硕士学位论文,2008.
[38] Bo Wu, Ram Nevatia, Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors, International Journal of Computer Vision, volume 75, Issue 2(November 2007), pages 247~266 ISSN:0920-5691
[39] Shashua A, Gdalyahu Y, Hayun G, Pedestrian detection for driving assistance systems: single-frame classification and system level performance. In: Proceedings of IEEE Intelligent Vehicles Symposium, Parma, Italy, IEEE, 2004, 1~6
[40] Gavrila. D M, Pedestrian detection from a moving vehicle. In: Proceedings of the 6th European Conference on Computer Vision-Part II. London, UK: Springer-Verlag, 2000. 37~49
[41]李颢,杨明.基于非线性逆透视变换的摄像机畸变参数标定.上海交通大学学报. 2008, 42(10):1736-1739
[42]刘大学,戴斌,李政,贺汉根.一种单线激光雷达和可见光摄像机的标定方法[J].华中科技大学学报:自然科学版.2008, 36(S1): 68-71.
[43]马颂德,张正友.计算机视觉——计算理论与算法基础[M].北京:科学出版社. 1998
[44] von der Hardt, H.-J.; Wolf, D.; Husson, R.; The dead reckoning localization system of the wheeled mobile robot ROMANE. In: Multisensor Fusion and Integration for Intelligent Systems, 1996. IEEE/SICE/RSJ International Conference on8-11 Dec. 1996 Page(s):603 - 610
[45] Abel Mendel, Urbano Nunes, Situation-based Multi-target Detection and Tracking with Laser-scanner in Outdoor Semi-structured Environment, Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, September 28– October 2, 2004, Sendai, Japan, 2004, pp. 88-93
[46] Sérgio Santos, JoséEduardo Faria, Fernando Soares, Rui Araújo and Urbano Nunes, Tracking of Multi-Obstacles with Laser Range Data For Autonomous Vehicle, ROBOTICA2003, May, 2003
[47]罗军辉,冯平,哈力旦.A. MATLAB7.0在图像处理中的应用[M].北京:机械工业出版社,2005.6
[48]刘瑞祯,于仕琪. OpenCV教程.基础篇[M].北京:北京航空航天大学出版社,2007.6
[49] J.M collado, C.Hilario, A.de la Escalera etc. Model based vehicle detection for intelligent vehicles, Parma, Italy: Procs IEEE Intelligent Vehicles Symposium, 2004:572-577
[50] Huachun Tan, Yu-Jin Zhang. Computing Eigenface from Edge Images for Face Recognition Based on Hausdorff Distance. Image and Graphics, 2007, ICIG 2007, Fourth International Conference on 22-24 Aug.2007 Pages: 639-644
[51] Nordsjo, A.E. Target tracking based on Kalman-type filters combined with recursive estimation of model disturbances; Radar Conference, 2005 IEEE International9-12 May 2005 Page(s):115– 120
[52]王惠南,GPS原理与应用,北京:科学出版社,2003年8月
[53]徐绍铨,张华海,杨志强,王泽民,GPS测量原理及应用,武汉大学出版社,2002年
[54]董锐,车辆监控系统中的坐标转换及数据库应用,哈尔滨工程大学,2003年
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