面向行人检测的组合分类计算模型与应用研究
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摘要
行人检测系统(Pedestrian Detection Systems,PDS)目的在于从行驶的汽车上,基于各种传感器,通过一些智能算法,实时而准确地判断出汽车前方是否有行人以及行人的大致位置,并进一步判断是否会发生碰撞并予以报警和紧急自动控制。这是一个从实际应用中提炼出来的关键问题,是汽车主动安全(ActiveSafety)、智能车辆(Smart Vehicle)和智能交通(Intelligent Transportation Systems)的一个重要组成部分,有着很强的应用背景和市场价值。同时,行人检测系统也是一个多学科交叉的研究热点,其研究涉及到传感、机器学习、自动化与控制、信息融合、计算智能等,因此也具有很高的科研意义。
目前的行人检测系统可以分为两大类,一类是以汽车厂商为代表的研究团队,他们尽可能采用红外、微波、激光等雷达设备,希望能借助这些昂贵的设备来降低处理难度,以获得更快的行人检测速度和精度;另一类研究者希望仅使用简单、廉价的光学设备,通过设计一些改进的算法来同样得到实时的检测速度和可接受的检测性能。研究者们通过对这些传感设备的比较分析,认为普通光学设备有着一些不可替代的优势,并且其核心技术(例如:分类算法)很容易推广到基于其它传感器的系统上,因此基于视觉的行人检测系统具有重要的理论研究意义和很高的实际应用价值。
本文立足于采用光学摄像头的行人检测系统与关键技术的研究。基于视觉系统的行人检测面临的主要难点在于:1)检测平台和检测对象的不规则自主运动;2)场景的多样性和时变性;3)行人的多样性以及部分遮挡的问题。这些难点导致分类技术作为一种用于行人检测的关键技术,还是一种有待突破的技术难题。同时,如何设计并搭建出一个具有实用性的基于视觉的行人检测原型系统也为研究界与产业界所急需。
行人检测中分类技术主要面临以下三个难点:1)对于检测过程,每帧中待检测的对象数量大,且行人所占比例极少:2)对于分类器训练,面临着样本不平衡问题;3)对于分类器性能要求,同时要求检测速度快、检测率高和误报率低:但这三点相互冲突,因而很难找到一个平衡点。在行人检测系统中,现有的分类方法还存在许多不足,主要包括:1)采用单分类器的方法检测率低,误报率高,检测速度低,多场景适应性差;2)采用串联组合分类器的方法误报率低、检测速度快,但检测率较低、多场景适应性差;3)采用并联组合分类器的方法检测率较高,误报率低,多场景适应性较好,但检测速度慢。
针对行人检测系统与关键技术存在的上述难点问题,我们认为有必要研究出针对性的高效分类模型与算法,设计面向应用的实现技术,最终搭建一个具有实用价值的行人检测原型系统。为此,本文围绕这一选题,完成了以下主要成果:
1.研究可以得到三方面性能平衡且综合性能最佳的组合分类模型。我们结合串并联组合分类器的优点,提出了树状组合分类计算模型(Tree ClassifierEnsemble Model),结合单分类器性能模型对组合分类器的三方面性能进行了定量的数学描述,从而将行人检测中的分类器设计转化为一个优化问题,进而使获得满足行人检测需求的高性能分类器成为可能。
2.研究面向应用的组合分类模型的实现技术。组合分类模型在应用时需要得到其中参数的合理取值才能保证综合性能最优。针对目前分类器关键参数只能通过实验调整的方法获得,不但需要花费很多时间,还不能保证得到的参数最优。为此,我们通过对样本不平衡性的分析,结合组合分类模型的三方面性能表达式,建立了其关键参数的计算模型(Computafion Model),使得保证组合分类器综合性能最优的关键参数可以直接求解得到,大大加速了全局寻优过程。此外,针对实际应用中参数计算模型可能不完全成立的情况,提出了采用RBF拟合结合穷举搜索的方式,仍能在可接受时间内搜索得到组合分类模型的全局最优参数,进一步加强了组合分类模型的应用范围并为该模型在其它分类问题中的应用指明了道路。
3.搭建一个具有实用价值的行人检测原型系统。针对智能交通市场对廉价的行人检测原型系统的需要,本文在组合分类计算模型的基础上,分别完成了基于PC计算平台的行人检测算法离线验证平台和基于DSP的车载行人检测原型系统。在离线平台上,除行人分类检测功能外,还实现了行人距离估计、碰撞预测和报警等功能,并且探索了双目视觉下的相关技术;在DSP平台上,结合Adaboost整型化等技术,实现了在计算能力较弱的平台上实时行人检测功能,该原型系统廉价、低功耗且体积小巧,为相关技术的市场化做好了技术储备。
Pedestrian Detection Systems(PDS) aim to detect and localize pedestrian in front of a moving vehicle accurately and quickly,with the help of some sensors and intelligent processing algorithms;and then to forecast the pedestrian-vehicle related collision;at last,to alert the driver and to make some emergency control if necessary. This is a hot research topic extracted from industry requirements;it is an important part of Active Safety,Smart Vehicle and Intelligent Transportation Systems,which has great research value and marketing value.At the same time,PDS is also a cross-research topic related to sensor technology,machine learning,automation and control,information fusion and computational intelligence.
At present,PDS research can be categorized into two types:1) researchers from auto industry trend to use expensive sensors,such as infrared cameras, millimeter-wave radars and laser scanners,to guarantee higher detection accuracy and speed;2) some other researchers prefer to develop simple and cheap solutions with optical cameras only.They expect to get acceptable detection accuracy and real-time detection speed with algorithm improvement.Some researchers considered that vision based PDS has some irreplaceable advantages and the key detection technologies(e.g. classification) can be easily extended to systems based on other sensors.Therefore, vision based PDS also has high research value and application value.
This thesis focuses on optical camera based pedestrian detection systems and key technologies.This type of PDS has following main difficulties:1) the autonomic and irregular movement of the detection platform and objects;2) the various ad time-variant scenes;3) the diversity of human appearance and partly-sheltered problem.Therefore,classification becomes a key technology for PDS which needs to be well developed.At the same time,a practical and low-cost vision based PDS antitype system is also great needed by researchers and auto industry.
Classification for PDS has three main difficulties:1) for the pedestrian detection problem,there are too many objects need to be classified in a single frame and most of them are non-pedestrians;2) for classifier training,the samples are imbalanced too; 3) classifiers for PDS need to satisfy the three conflicting demands at the same time, and it is hard to find a balance point of high detection rate,low false positive rate and high detection speed.At present,classifiers adopted in PDS have following shortcomings:1) single classifiers have imbalanced performances(e.g.low detection rate,high false positive rare and low detection speed),and they are not suitable for varying scenes.2) serial ensemble classifiers have low false positive rate and high detection rate;however,their detection rate is comparatively low and still not suitable for varying scenes.3) parallel ensemble classifiers have high detection rate and low false positive rate,and can be applied in varying scenes;however,their detection speed is very low and can not satisfy practical requirements.
To solve the difficult problems mentioned above and to conquer the shortcomings of existing technologies,we considered it necessary to purposely design a high-performance classification model and corresponding algorithm at first;and then to study its application methods;at last,to build up a practical on-board PDS antitype system based on the proposed model and algorithm.Around this research topic,several contributions are made in this thesis:
1.In order to get a classifier with banlanced and optimal overall performance, tree classifier ensemble model was proposed,which has both advantages of serial classifier ensemble and parallel classifier ensemble.With the single classifier performance model,the quantified expressions of three performance indicators of the tree classifier ensemble can be obtained,and then the classifier design problem turns to an optimization problem.This makes a high performance classifier for practical PDS applications possible.
2.The classifier ensemble can get the optimal performance only when proper values are chosen for the key parameters.At present,these parameters are tuned by repeat experiments which cost too much time and still can not get the global optimal values.In this thesis,a computational model was built for the tree classifier ensemble based on the character of sample imbalance.The key parameters can be calculated directly with the computational model,and this greatly accelerates the optimal values searching and guarantees the balanced and optimal performance of the classifier ensemble.Furthermore,a RBF based searching method is proposed to solve the situation when the computational model is not well suitable;this method can also find the global optimal values of the classification model with acceptable time cost.This method shows that the computational model might be used in other backgrounds.
3.To satisfy the PDS antitype system requirements of ITS market,two systems are developed based on the computational classifier ensemble model.One is based on PC platform,which is used for offline algorithm verification,and the other one is based on a DSP chip,which is used for on-board pedestrian detection.The PC based system has functions of pedestrian localization,collision forecasting and alarm besides pedestrian detection;furthermore,pedeatrian detection technologies based on dual optical cameras are also tested on this system.The DSP based system can perform real-time pedestrian detection with the help of some technologies such as integral Adaboost algorithm;it is low-cost,power-saving and size compacted,which makes PDS marketization possible.
目前的行人检测系统可以分为两大类,一类是以汽车厂商为代表的研究团队,他们尽可能采用红外、微波、激光等雷达设备,希望能借助这些昂贵的设备来降低处理难度,以获得更快的行人检测速度和精度;另一类研究者希望仅使用简单、廉价的光学设备,通过设计一些改进的算法来同样得到实时的检测速度和可接受的检测性能。研究者们通过对这些传感设备的比较分析,认为普通光学设备有着一些不可替代的优势,并且其核心技术(例如:分类算法)很容易推广到基于其它传感器的系统上,因此基于视觉的行人检测系统具有重要的理论研究意义和很高的实际应用价值。
本文立足于采用光学摄像头的行人检测系统与关键技术的研究。基于视觉系统的行人检测面临的主要难点在于:1)检测平台和检测对象的不规则自主运动;2)场景的多样性和时变性;3)行人的多样性以及部分遮挡的问题。这些难点导致分类技术作为一种用于行人检测的关键技术,还是一种有待突破的技术难题。同时,如何设计并搭建出一个具有实用性的基于视觉的行人检测原型系统也为研究界与产业界所急需。
行人检测中分类技术主要面临以下三个难点:1)对于检测过程,每帧中待检测的对象数量大,且行人所占比例极少:2)对于分类器训练,面临着样本不平衡问题;3)对于分类器性能要求,同时要求检测速度快、检测率高和误报率低:但这三点相互冲突,因而很难找到一个平衡点。在行人检测系统中,现有的分类方法还存在许多不足,主要包括:1)采用单分类器的方法检测率低,误报率高,检测速度低,多场景适应性差;2)采用串联组合分类器的方法误报率低、检测速度快,但检测率较低、多场景适应性差;3)采用并联组合分类器的方法检测率较高,误报率低,多场景适应性较好,但检测速度慢。
针对行人检测系统与关键技术存在的上述难点问题,我们认为有必要研究出针对性的高效分类模型与算法,设计面向应用的实现技术,最终搭建一个具有实用价值的行人检测原型系统。为此,本文围绕这一选题,完成了以下主要成果:
1.研究可以得到三方面性能平衡且综合性能最佳的组合分类模型。我们结合串并联组合分类器的优点,提出了树状组合分类计算模型(Tree ClassifierEnsemble Model),结合单分类器性能模型对组合分类器的三方面性能进行了定量的数学描述,从而将行人检测中的分类器设计转化为一个优化问题,进而使获得满足行人检测需求的高性能分类器成为可能。
2.研究面向应用的组合分类模型的实现技术。组合分类模型在应用时需要得到其中参数的合理取值才能保证综合性能最优。针对目前分类器关键参数只能通过实验调整的方法获得,不但需要花费很多时间,还不能保证得到的参数最优。为此,我们通过对样本不平衡性的分析,结合组合分类模型的三方面性能表达式,建立了其关键参数的计算模型(Computafion Model),使得保证组合分类器综合性能最优的关键参数可以直接求解得到,大大加速了全局寻优过程。此外,针对实际应用中参数计算模型可能不完全成立的情况,提出了采用RBF拟合结合穷举搜索的方式,仍能在可接受时间内搜索得到组合分类模型的全局最优参数,进一步加强了组合分类模型的应用范围并为该模型在其它分类问题中的应用指明了道路。
3.搭建一个具有实用价值的行人检测原型系统。针对智能交通市场对廉价的行人检测原型系统的需要,本文在组合分类计算模型的基础上,分别完成了基于PC计算平台的行人检测算法离线验证平台和基于DSP的车载行人检测原型系统。在离线平台上,除行人分类检测功能外,还实现了行人距离估计、碰撞预测和报警等功能,并且探索了双目视觉下的相关技术;在DSP平台上,结合Adaboost整型化等技术,实现了在计算能力较弱的平台上实时行人检测功能,该原型系统廉价、低功耗且体积小巧,为相关技术的市场化做好了技术储备。
Pedestrian Detection Systems(PDS) aim to detect and localize pedestrian in front of a moving vehicle accurately and quickly,with the help of some sensors and intelligent processing algorithms;and then to forecast the pedestrian-vehicle related collision;at last,to alert the driver and to make some emergency control if necessary. This is a hot research topic extracted from industry requirements;it is an important part of Active Safety,Smart Vehicle and Intelligent Transportation Systems,which has great research value and marketing value.At the same time,PDS is also a cross-research topic related to sensor technology,machine learning,automation and control,information fusion and computational intelligence.
At present,PDS research can be categorized into two types:1) researchers from auto industry trend to use expensive sensors,such as infrared cameras, millimeter-wave radars and laser scanners,to guarantee higher detection accuracy and speed;2) some other researchers prefer to develop simple and cheap solutions with optical cameras only.They expect to get acceptable detection accuracy and real-time detection speed with algorithm improvement.Some researchers considered that vision based PDS has some irreplaceable advantages and the key detection technologies(e.g. classification) can be easily extended to systems based on other sensors.Therefore, vision based PDS also has high research value and application value.
This thesis focuses on optical camera based pedestrian detection systems and key technologies.This type of PDS has following main difficulties:1) the autonomic and irregular movement of the detection platform and objects;2) the various ad time-variant scenes;3) the diversity of human appearance and partly-sheltered problem.Therefore,classification becomes a key technology for PDS which needs to be well developed.At the same time,a practical and low-cost vision based PDS antitype system is also great needed by researchers and auto industry.
Classification for PDS has three main difficulties:1) for the pedestrian detection problem,there are too many objects need to be classified in a single frame and most of them are non-pedestrians;2) for classifier training,the samples are imbalanced too; 3) classifiers for PDS need to satisfy the three conflicting demands at the same time, and it is hard to find a balance point of high detection rate,low false positive rate and high detection speed.At present,classifiers adopted in PDS have following shortcomings:1) single classifiers have imbalanced performances(e.g.low detection rate,high false positive rare and low detection speed),and they are not suitable for varying scenes.2) serial ensemble classifiers have low false positive rate and high detection rate;however,their detection rate is comparatively low and still not suitable for varying scenes.3) parallel ensemble classifiers have high detection rate and low false positive rate,and can be applied in varying scenes;however,their detection speed is very low and can not satisfy practical requirements.
To solve the difficult problems mentioned above and to conquer the shortcomings of existing technologies,we considered it necessary to purposely design a high-performance classification model and corresponding algorithm at first;and then to study its application methods;at last,to build up a practical on-board PDS antitype system based on the proposed model and algorithm.Around this research topic,several contributions are made in this thesis:
1.In order to get a classifier with banlanced and optimal overall performance, tree classifier ensemble model was proposed,which has both advantages of serial classifier ensemble and parallel classifier ensemble.With the single classifier performance model,the quantified expressions of three performance indicators of the tree classifier ensemble can be obtained,and then the classifier design problem turns to an optimization problem.This makes a high performance classifier for practical PDS applications possible.
2.The classifier ensemble can get the optimal performance only when proper values are chosen for the key parameters.At present,these parameters are tuned by repeat experiments which cost too much time and still can not get the global optimal values.In this thesis,a computational model was built for the tree classifier ensemble based on the character of sample imbalance.The key parameters can be calculated directly with the computational model,and this greatly accelerates the optimal values searching and guarantees the balanced and optimal performance of the classifier ensemble.Furthermore,a RBF based searching method is proposed to solve the situation when the computational model is not well suitable;this method can also find the global optimal values of the classification model with acceptable time cost.This method shows that the computational model might be used in other backgrounds.
3.To satisfy the PDS antitype system requirements of ITS market,two systems are developed based on the computational classifier ensemble model.One is based on PC platform,which is used for offline algorithm verification,and the other one is based on a DSP chip,which is used for on-board pedestrian detection.The PC based system has functions of pedestrian localization,collision forecasting and alarm besides pedestrian detection;furthermore,pedeatrian detection technologies based on dual optical cameras are also tested on this system.The DSP based system can perform real-time pedestrian detection with the help of some technologies such as integral Adaboost algorithm;it is low-cost,power-saving and size compacted,which makes PDS marketization possible.
引文
岑峰.2002.视频监控系统中面向人的目标跟踪技术的研究[D].上海:上海交通大学.
郭烈,王荣本,顾柏园,余天洪.2005.世界智能车辆行人检测技术综述[J].公路交通科技,22:133-137.
贾慧星,章毓晋.2007.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[J].自动化学报,33:84-90.
田广.2007.基于视觉的行人检测和跟踪技术的研究[D].上海:上海交通大学.
魏闯先.2008.面向行人检测的组合分类器设计[D].合肥:中国科学技术大学.
许言午,曹先彬,乔红.2008.行人检测系统研究新进展及关键技术展望[J].电子学报,36(5):962-968.
张琦,吴斌,王柏.2005.非平衡数据训练方法概述[J].计算机科学,32(10):181-186.
朱文佳.2008.基于机器学习的行人检测关键技术研究[D].上海:上海交通大学.
朱谊强,张洪才,程咏梅,杨涛,赵春晖.2006.基于Adaboost算法的实时行人检测系统[J].计算机测量与控制,14(11):1462-1465.
Abramson Y and Steux B.2004.Hardware-friendly pedestrian detection and impact prediction [C]// Proceedings of IEEE International Conference on Intelligent Vehicles Symposium.Parma,Italy:IEEE:590-595.
Broggi A,Bertozzi M,Fascioli A and Conte G.1999.Automatic Vehicle Guidance:the experience of the ARGO Autonomous Vehicle[M].USA:World Scientific Publishing Company.
Broggi A,Bertozzi M,Fascioli A,Sechi M.2000.Shape-based pedestrian detection[C]//IEEE Intelligent Vehicles Symposium.Dearborn,USA:IEEE:215-220.
Bertozzi M,Broggi A,Lasagni A and Rose M D.2005.Infrared stereo vision-based pedestrian detection[C]//Proceedings of IEEE International Conference on Intelligent Vehicles Symposium.Las Vegas,USA:IEEE:24-29.
Cao X B,Qiao H and Keane J.2008.A low-cost pedestrian detection system with a single optical camera[J].IEEE Transactions on Intelligent Transportation System,9(1):58-67.
Cao X B,Qiao H,Wang F Y and Zhang X Z.2005.Application of Cooperative Co-evolution in Pedestrian Detection Systems[C]//Proeeedings of IEEE International Conference on Intelligence and Security Informatics.Atlanta,GA,USA:IEEEE,3495:664-665.
Chang C C and Lin C J.2001.LIBSVM:a library for support vector machines.Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
Chawla N V,Japkowicz N and Kolcz A.2004.Editorial:Special Issue on Learning from Imbalanced Data Sets [J].Newsletter of the ACM Special Interest Group on Knowledge Discovery and Data Mining,6(1):1-6.
Chawla N V,Lazarevic A,Hall L O and Bowyer Kevin.2003.SMOTEBoost:Improving Prediction of the Minority Class in Boosting [C]//Proceedings of the 7~(th)European Conference on Principles and Practice of Knowledge Discovery in Databases.Cavtat-Dubrovnik,Croatia:Springer Berlin:107-119.
Chen D,Cao X B,Xu Y W,Qiao H and Wang F Y.2006.A SVM-based classifier with shape and motion features for pedestrian detection system [C]//Proceedings of IEEE Intelligent Vehicle Symposium.Tokyo,Japan:IEEE:331-335.
Cheng H,Zheng N N and Qin J J.2005.Pedestrian detection using sparse Gabor filter and support vector machine [C]//Proceedings of IEEE Intelligent Vehicles Symposium.Las Vegas,USA:IEEE:583-587.
Cui X Y,Liu Y Z,,Shan S G,Chen X L and Gao W.2007.3D Haar-Like Features for Pedestrian Detection [C]// IEEE International Conference on Multimedia and Expo.Bejing,China:IEEE:1263-1266.
Cutler R and Davis L S.2000.Robust real-time periodic motion detection,analysis and applications [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,22:781-796
Curio C,Edelbrunner J,Kalinke T,Tzomakas C,Bruckhoff C,Bergener T and Seelen W.1999.Walking pedestrian detection and classification [C]// Proceedings of the DAGM-Symposium.Bonn,Germany:Mustererkennung:78-85.
Curio C,Edelbrunner J,Kalinke T,Tzomakas C,Bruckhoff C,Bergener T and Seelen W.2000.Walking pedestrian recognition [J].IEEE Transactions on Intelligent Transport Systems,1:155-163.
Dai C X,Zheng Y F and Li X.2007.Pedestrian detection and tracking in infrared imagery using shape and appearance [J].Computer Vision and Image Understanding,106(2-3):288-299.
Dalai N and Triggs B.2005.Histograms of oriented gradients for human detection [C]//IEEE International Conference on Computer Vision and Pattern Recognition.San Diego,CA,USA: IEEE,1:886-893.
Elzein H,Lakshmanan S,Watta P.2004.A motion and shape-based pedestrian detection algorithm [C]//Proceedings of IEEE International Conference on Intelligent Vehicles Symposium.Columbus,USA:IEEE:500-504.
Fang Y,Yamada K,Ninomiya Y,Horn B and Masaki I.2003.Comparison between infrared-image-based and visible-imagebased approaches for pedestrian detection [C]//Proceedings of the IEEE Intelligent Vehicle Symposium.Ohio,USA:IEEE:505-510.
Fardi B,Schuenert U and Wanielik G.2005.Shape and motion-based pedestrian detection in infrared images:A multi sensor approach [C]//Proceedings of IEEE International Conference on Intelligent Vehicles Symposium.Las Vegas,USA:IEEE:18-23.
Franke U,Gavrila D M,G(o|¨)rzig S,Lindner F,Paetzold F and W(o|¨)hler C.1998.Autonomous driving goes downtown [C]//IEEE International Conference on Intelligent System.13(6):32-40.
Franke U and Joos A.2000.Real-time stereo vision for urban traffic scene understanding.In:Proceedings of IEEE Intelligent Vehicles Symposium.Dearborn,MI.IEEE,273-278.
Freund Y and Schapire R E.1997.A Decision-Theoretic generalization of on-line learning and an application to boosting [J].Journal of Computer and System Sciences,55:11-139.
Fuerstenberg K C,Dietmayer K C J and Willhoeft V.2002.Pedestrian recognition in urban traffic using a vehicle based multilayer Laserscanner [C]//Proceedings of IEEE Intelligent Vehicles Symposium.Versailles,France:IEEE:31-35.
Gandhi T and Trivedi M M.2006.Pedestrian collision avoidance systems:a survey of computer vision based recent studies [C]//Proceeding of Intelligent Transportation Systems Conference.Toronto,Canada:IEEE:976-981.
Gavrila D M.2000.Pedestrian detection from a moving vehicle [C]//European Conference on Computer Vision.Dublin,Ireland:Springer:37-49.
Gavrila D M and Giebel J.2003.Shape-based pedestrian detection and tracking [C]//IEEE Intelligent Vehicles Symposium.Columbus,OH,USA:IEEE:8-14.
Gavrila D M,Giebel J and Munder S.2004.Vision-based pedestrian detection:the PROTECTOR system [C]//Proceedings of IEEE Intelligent Vehicles Symposium.Parma,Italy:IEEE:13-18.
Gavrila D M and Munder S.2007.Multi-Cue Pedestrian Detection and Tracking from a Moving Vehicle [J].International Journal of Computer Vision,73(1):41-59.
Ge J F,Luo Y P and Xiao D Y.2006.Adaptive hysteresis thresholding based pedestrian detection in nighttime using a normal camera [C]//Proceedings of IEEE International Conference on Vehicular Electronics and Safety.Shanghai,China:IEEE:46-51.
Grubb G,Zelinsky A,Nilsson L and Rilbe M.2004.3D Vision Sensing for Improved Pedestrian Safety [C]//Proceedings of IEEE International Conference on Intelligent Vehicle Symposium.Parma,Italy:IEEE:19-24.
Guo L,Wang R B,Jin L S,Li L H and Yang L.2006.Algorithm Study for Pedestrian Detection Based on Monocular Vision [C]//Proceedings of IEEE International Conference on Vehicular Electronics and Safety.Shanghai,China:IEEE:83-87.
Guo Y P,Cao X B,Xu Y W and Qiao H.2007.Co-Evolution based Feature Selection for Pedestrian Detection [C]//Proceedings of IEEE International Conference on Control and Automation.Guangzhou,China:IEEE:2797-2801.
Harris C.and Stephens M.1988.A combined corner and edge detector [C]//Proceedings of the 4 Alvey Vision Conference.Manchester,UK:Springer:147-151.
Havasi L,Szl(?)vik Z and Sziranyi T.2006.Pedestrian detection using derived third-order symmetry of legs [J].Computational Imaging and Vision.32:733-739.
Heisele B and W6hler C.1998.Motion-based recognition of pedestrians [C]//Proceedings of IEEE International Conference on Pattern Recognition.Brisbane,Australia:IEEE,2:1325-1330.
Jain A,Duin R.and Mao J.2000.Statistical Pattern Recognition:A Review [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,22:4-37.
Jung H G,Lee Y H,Yoon P J,Hwang I Y and Kim J.2006.Sensor fusion based obstacle detection/classification for active pedestrian protection system [C]//Proceedings of Advances in Visual Computing.Lake Tahoe,NV,USA:Springer,4292:294-305.
Kamijo S and SakauchiM.2003.Simultanious tracking of pedestrians and vehicles by the spatio-temporal Markov Random Field Model [C]//IEEE International Conference on Systems,Man and Cybernetics.Washington D.C.,USA:IEEE,4(5-8):3732-3737.
Kato T,Ninomiya Y and Masaki I.2002.An obstacle detection method by fusion of radar and motion stereo [J],IEEE Transactions on Intelligent Transportation Systems,3:182-188.
Koenderink J J.1984.The structure of images [J].Biological Cybernetics.50 (5):363-396.
Lienhart R and Maydt J.2000.An extended set of Haar-like features for rapid object detection//Proceedings of IEEE International Conference on Image Processing.New York,USA:IEEE,9:900-903.
Lindeberg T.1994.Scale-space theory:A basic tool for analysing structures at different scales [J],Journal of Applied Statistics.21(2):224-270.
Lipton A.1999.Local Application of Optic flow to Analyze Rigid Versus Non-rigid Motion [C]//Proceedings of International Conference on Computer Vision Workshop on Frame-Rate Vision.Corfu,Greece:IEEE,http://www.ri.cmu.edu/pubs/pub_2934.html.
Lowe D M.2004.Distinctive image features from scale-invariant keypoints [J].International Journal of Computer Vision,60(2):91-110.
Marill T and Green D.1963.On the e6ectiveness of receptors in recognition systems [J].IEEE Transactions on Information Theory,9:11-17.
Miao G X,Luo Y P,Tian Q M and Tang J X.2006.A filter module used in pedestrian detection system [C]//3~(rd)IFIP Conference on Artificial Intelligence Applications and Innovation.Athens,Greece:Springer Verlag:212-220.
Mikolajczyk K,Schmid C and Zisserman A.2004.Human detection based on a probabilistic assembly of robust part detectors [C]//Proceedings of European Conference on Computer Vision.Prague,Czech Republic:Springer:69-82.
Mohan A,Papageorgiou C and Poggio T.2001.Example-based object detection in images by components.IEEE Transactions on Pattern Analysis and Machine Intelligence [J],23(4):349-361.
Munder S and Gavrila D M.2006.An Experimental Study on Pedestrian Classification [J].Transactions on Pattern Analysis and Machine Intelligence,28(11):1863-1868.
Oren M,Papageorgiou C,Sinha P,Osuna E and Poggio T.1997.Pedestrian detection using wavelet templates [C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.San Juan,Puerto Rico:IEEE:193-199.
Papageorgiou C,Oren M and Poggio T.1998.A general framework for Object Detection [C]//International Conference on Computer Vision.Bombay,India:IEEE:555-562.
Papageorgiou C and Poggio T,1999.Trainable pedestrian detection [J].Image Processing.35-39.
Papageorgiou C and Poggio T.2000.A trainable system for object detection [J].International Journal of Computer Vision,38(1):15-33.
Parra I,Fernandez D,Sotelo M A,Bergasa L M,Revenga P,Nuevo J,Ocana M and Garcia-Garrido M A.2007.Combination of feature extraction methods for SVM pedestrian detection [J].IEEE Transactions on Intelligent Transportation Systems,8(2):292-307.
Pudil P,Novovicova J and Kittler J.1994.Floating search methods in feature selection [J].Pattern Recognition Letters,15:1119-1125.
Sabzmeydani P and Mori G 2007.Detecting Pedestrians by Learning Shapelet Features [C]//IEEE Conference on Computer Vision and Pattern Recognition.Minneapolis,Minnesota,USA:IEEE:1-8.
Siedlecki W and Sklansky J.1988.On automatic feature selection [J].International Journal of Pattern Recognition and Artificial Intelligence,2:197-220.
Ran Y,Zheng Q F,Weiss I,Davis L S,Abd-Almageed W and Zhao L.2005.Pedestrian classification from moving platforms using cyclic motion pattern [C]//Proceedings of International Conference on Image Processing.Genova,Italy:IEEE:2173-2176.
Raskutti B and Kowalczyk A.2004.Extreme Rebalancing for SVMs:a case study [J].Newsletter of the ACM Special Interest Group on Knowledge Discovery and Data Mining,6(1):60-69.
Refregier A.2003.Shapelets:I.A method for image analysis [J].Monthly Notice of the Royal Astronomical Society.338(1):35-47.
Rowley H A,Baluja S and Kanade T.1998.Neural networkbased face detection [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,20(1):23-38.
Schauland S and Kummert A.2007.Implementation and optimization of wavelet and symmetry features for vision-based pedestrian detection [C]//Proceedings of International Conference on Graphics and Visualization in Engineering.Clearwater,Florida,USA:Springer:19-24.
Shashua A,Gdalyahu Y and Hayun G 2004.Pedestrian Detection for Driving Assistance Systems-Single-frame Classification and System Level Performance [C]//Proceedings of IEEE International Conference on Intelligent Vehicles Symposium.Parma,Italy:IEEE:1-6.
Sun H,Hua C Y and Luo Y P.2003.A multi-stage classifier based algorithm of pedestrian detection in night with a near infrared camera in a moving car [C]//Proceedings of the 3~(rd)International Conference on Image and Graphics.Hong Kong,China:Springer:734-741.
Szarvas M,Yoshizawa A,Yamamoto M and Ogata J.2005.Pedestrian detection with convolutional neural networks [C]//Proceedings of IEEE Intelligent Vehicles Symposium.Las Vegas,Nevada:IEEE:224-229.
Tian Q M,Sun H,Luo Y P,Hu D C.2005.Nighttime pedestrian detection with a normal camera using SVM classifier [C]//Proceedings of International Symposium on Neural Networks.
Chongqing,China:Springer:3497:189-194.
Tons M,Doerfler R,Meinecke M M,Obojski M A.2004.Radar sensors and sensor platform used for pedestrian protection in the EC-funded project SAVE-U [C]//IEEE Intelligent Vehicles Symposium.Parma,Italy:IEEE:813-818.
Torresan H,Turgeon B,Ibarra-Castanedo C,Hebert P and Maldague X.2004.Advanced surveillance systems:Combining video and thermal imagery for pedestrian detection [C]//Proceedings of the Society of Photo-optical Instrumentation Engineers.5405:506-515.
Vapnik V.1999.The Nature of Statistical Learning Theory [M].Berlin:Springer-Verlag.
Viola P and Jones M.2001.Rapid Object detection using a boosted cascade of simple features [C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Marriott,Hawaii:IEEE,1:511-518.
Viola P,Jones M and Snow D.2003.Detecting pedestrians using patterns of motion and appearance [C]//Proceedings of International Conference on Computer Vision.Nice,France:Springer:734-741.
Viola P,Jones M and Snow D.2005.Detecting pedestrians using patterns of motion and appearance [J].International Journal of Computer Vision,63(2):153-161.
Wei C X,Cao X B,Xu Y W,Qiao H and Wang F Y.2007.The Treelike Assembly Classifier for Pedestrian Detection [C]//Proceedings of Pacific Asian Workshop on Intelligence and Security Informatics.Chengdu,China:Springer:232-237.
Weiss G M.2004.Mining with Rarity:A Unifying Framework [J].Newsletter of the ACM Special Interest Group on Knowledge Discovery and Data Mining,6(1):7-19.
W(O|¨)hler C,Kressler U and Anlauf J K.2000.Pedestrian recognition by classification of image sequences:global approaches vs.local spatio-temporal processing [C]//Proceedings of 15~(th)International Conference on Pattern Recognition.Barcelona,Spain:IEEE,2:540-544.
Xu F L,Liu X and Fujimura K.2005.Pedestrian detection and tracking with night vision [J].IEEE Transactions on Intelligent Transportation Systems,6:63-71.
Xu Y W,Cao X B,Qiao H.2006.Optical Camera Based Pedestrian Detection in Rainy or Snowy Weather [C]//Proceedings of IEEE International Conference on Fuzzy Systems and Knowledge Discovery.Xi'an,China:Springer,4223:1182-1191.
Zhao L and Thorpe C.2000.Stereo and neural network-based pedestrian detection [J].IEEE Transactions on Intelligent Transportation Systems,1:148-154.
Zhao J and Yao D Y.2006.Occlusion adaptive object tracking based on video image and radar data [C].Proceedings of IEEE International Conference on ITS Telecommunications.Chengdu,China:IEEE:943-946.
Zheng Z H,Wu X Y and Srihari R.2004.Feature Selection for Text Categorization on Imbalanced Data [J].Newsletter of the ACM Special Interest Group on Knowledge Discovery and Data Mining,6(1):80-88.
郭烈,王荣本,顾柏园,余天洪.2005.世界智能车辆行人检测技术综述[J].公路交通科技,22:133-137.
贾慧星,章毓晋.2007.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[J].自动化学报,33:84-90.
田广.2007.基于视觉的行人检测和跟踪技术的研究[D].上海:上海交通大学.
魏闯先.2008.面向行人检测的组合分类器设计[D].合肥:中国科学技术大学.
许言午,曹先彬,乔红.2008.行人检测系统研究新进展及关键技术展望[J].电子学报,36(5):962-968.
张琦,吴斌,王柏.2005.非平衡数据训练方法概述[J].计算机科学,32(10):181-186.
朱文佳.2008.基于机器学习的行人检测关键技术研究[D].上海:上海交通大学.
朱谊强,张洪才,程咏梅,杨涛,赵春晖.2006.基于Adaboost算法的实时行人检测系统[J].计算机测量与控制,14(11):1462-1465.
Abramson Y and Steux B.2004.Hardware-friendly pedestrian detection and impact prediction [C]// Proceedings of IEEE International Conference on Intelligent Vehicles Symposium.Parma,Italy:IEEE:590-595.
Broggi A,Bertozzi M,Fascioli A and Conte G.1999.Automatic Vehicle Guidance:the experience of the ARGO Autonomous Vehicle[M].USA:World Scientific Publishing Company.
Broggi A,Bertozzi M,Fascioli A,Sechi M.2000.Shape-based pedestrian detection[C]//IEEE Intelligent Vehicles Symposium.Dearborn,USA:IEEE:215-220.
Bertozzi M,Broggi A,Lasagni A and Rose M D.2005.Infrared stereo vision-based pedestrian detection[C]//Proceedings of IEEE International Conference on Intelligent Vehicles Symposium.Las Vegas,USA:IEEE:24-29.
Cao X B,Qiao H and Keane J.2008.A low-cost pedestrian detection system with a single optical camera[J].IEEE Transactions on Intelligent Transportation System,9(1):58-67.
Cao X B,Qiao H,Wang F Y and Zhang X Z.2005.Application of Cooperative Co-evolution in Pedestrian Detection Systems[C]//Proeeedings of IEEE International Conference on Intelligence and Security Informatics.Atlanta,GA,USA:IEEEE,3495:664-665.
Chang C C and Lin C J.2001.LIBSVM:a library for support vector machines.Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
Chawla N V,Japkowicz N and Kolcz A.2004.Editorial:Special Issue on Learning from Imbalanced Data Sets [J].Newsletter of the ACM Special Interest Group on Knowledge Discovery and Data Mining,6(1):1-6.
Chawla N V,Lazarevic A,Hall L O and Bowyer Kevin.2003.SMOTEBoost:Improving Prediction of the Minority Class in Boosting [C]//Proceedings of the 7~(th)European Conference on Principles and Practice of Knowledge Discovery in Databases.Cavtat-Dubrovnik,Croatia:Springer Berlin:107-119.
Chen D,Cao X B,Xu Y W,Qiao H and Wang F Y.2006.A SVM-based classifier with shape and motion features for pedestrian detection system [C]//Proceedings of IEEE Intelligent Vehicle Symposium.Tokyo,Japan:IEEE:331-335.
Cheng H,Zheng N N and Qin J J.2005.Pedestrian detection using sparse Gabor filter and support vector machine [C]//Proceedings of IEEE Intelligent Vehicles Symposium.Las Vegas,USA:IEEE:583-587.
Cui X Y,Liu Y Z,,Shan S G,Chen X L and Gao W.2007.3D Haar-Like Features for Pedestrian Detection [C]// IEEE International Conference on Multimedia and Expo.Bejing,China:IEEE:1263-1266.
Cutler R and Davis L S.2000.Robust real-time periodic motion detection,analysis and applications [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,22:781-796
Curio C,Edelbrunner J,Kalinke T,Tzomakas C,Bruckhoff C,Bergener T and Seelen W.1999.Walking pedestrian detection and classification [C]// Proceedings of the DAGM-Symposium.Bonn,Germany:Mustererkennung:78-85.
Curio C,Edelbrunner J,Kalinke T,Tzomakas C,Bruckhoff C,Bergener T and Seelen W.2000.Walking pedestrian recognition [J].IEEE Transactions on Intelligent Transport Systems,1:155-163.
Dai C X,Zheng Y F and Li X.2007.Pedestrian detection and tracking in infrared imagery using shape and appearance [J].Computer Vision and Image Understanding,106(2-3):288-299.
Dalai N and Triggs B.2005.Histograms of oriented gradients for human detection [C]//IEEE International Conference on Computer Vision and Pattern Recognition.San Diego,CA,USA: IEEE,1:886-893.
Elzein H,Lakshmanan S,Watta P.2004.A motion and shape-based pedestrian detection algorithm [C]//Proceedings of IEEE International Conference on Intelligent Vehicles Symposium.Columbus,USA:IEEE:500-504.
Fang Y,Yamada K,Ninomiya Y,Horn B and Masaki I.2003.Comparison between infrared-image-based and visible-imagebased approaches for pedestrian detection [C]//Proceedings of the IEEE Intelligent Vehicle Symposium.Ohio,USA:IEEE:505-510.
Fardi B,Schuenert U and Wanielik G.2005.Shape and motion-based pedestrian detection in infrared images:A multi sensor approach [C]//Proceedings of IEEE International Conference on Intelligent Vehicles Symposium.Las Vegas,USA:IEEE:18-23.
Franke U,Gavrila D M,G(o|¨)rzig S,Lindner F,Paetzold F and W(o|¨)hler C.1998.Autonomous driving goes downtown [C]//IEEE International Conference on Intelligent System.13(6):32-40.
Franke U and Joos A.2000.Real-time stereo vision for urban traffic scene understanding.In:Proceedings of IEEE Intelligent Vehicles Symposium.Dearborn,MI.IEEE,273-278.
Freund Y and Schapire R E.1997.A Decision-Theoretic generalization of on-line learning and an application to boosting [J].Journal of Computer and System Sciences,55:11-139.
Fuerstenberg K C,Dietmayer K C J and Willhoeft V.2002.Pedestrian recognition in urban traffic using a vehicle based multilayer Laserscanner [C]//Proceedings of IEEE Intelligent Vehicles Symposium.Versailles,France:IEEE:31-35.
Gandhi T and Trivedi M M.2006.Pedestrian collision avoidance systems:a survey of computer vision based recent studies [C]//Proceeding of Intelligent Transportation Systems Conference.Toronto,Canada:IEEE:976-981.
Gavrila D M.2000.Pedestrian detection from a moving vehicle [C]//European Conference on Computer Vision.Dublin,Ireland:Springer:37-49.
Gavrila D M and Giebel J.2003.Shape-based pedestrian detection and tracking [C]//IEEE Intelligent Vehicles Symposium.Columbus,OH,USA:IEEE:8-14.
Gavrila D M,Giebel J and Munder S.2004.Vision-based pedestrian detection:the PROTECTOR system [C]//Proceedings of IEEE Intelligent Vehicles Symposium.Parma,Italy:IEEE:13-18.
Gavrila D M and Munder S.2007.Multi-Cue Pedestrian Detection and Tracking from a Moving Vehicle [J].International Journal of Computer Vision,73(1):41-59.
Ge J F,Luo Y P and Xiao D Y.2006.Adaptive hysteresis thresholding based pedestrian detection in nighttime using a normal camera [C]//Proceedings of IEEE International Conference on Vehicular Electronics and Safety.Shanghai,China:IEEE:46-51.
Grubb G,Zelinsky A,Nilsson L and Rilbe M.2004.3D Vision Sensing for Improved Pedestrian Safety [C]//Proceedings of IEEE International Conference on Intelligent Vehicle Symposium.Parma,Italy:IEEE:19-24.
Guo L,Wang R B,Jin L S,Li L H and Yang L.2006.Algorithm Study for Pedestrian Detection Based on Monocular Vision [C]//Proceedings of IEEE International Conference on Vehicular Electronics and Safety.Shanghai,China:IEEE:83-87.
Guo Y P,Cao X B,Xu Y W and Qiao H.2007.Co-Evolution based Feature Selection for Pedestrian Detection [C]//Proceedings of IEEE International Conference on Control and Automation.Guangzhou,China:IEEE:2797-2801.
Harris C.and Stephens M.1988.A combined corner and edge detector [C]//Proceedings of the 4 Alvey Vision Conference.Manchester,UK:Springer:147-151.
Havasi L,Szl(?)vik Z and Sziranyi T.2006.Pedestrian detection using derived third-order symmetry of legs [J].Computational Imaging and Vision.32:733-739.
Heisele B and W6hler C.1998.Motion-based recognition of pedestrians [C]//Proceedings of IEEE International Conference on Pattern Recognition.Brisbane,Australia:IEEE,2:1325-1330.
Jain A,Duin R.and Mao J.2000.Statistical Pattern Recognition:A Review [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,22:4-37.
Jung H G,Lee Y H,Yoon P J,Hwang I Y and Kim J.2006.Sensor fusion based obstacle detection/classification for active pedestrian protection system [C]//Proceedings of Advances in Visual Computing.Lake Tahoe,NV,USA:Springer,4292:294-305.
Kamijo S and SakauchiM.2003.Simultanious tracking of pedestrians and vehicles by the spatio-temporal Markov Random Field Model [C]//IEEE International Conference on Systems,Man and Cybernetics.Washington D.C.,USA:IEEE,4(5-8):3732-3737.
Kato T,Ninomiya Y and Masaki I.2002.An obstacle detection method by fusion of radar and motion stereo [J],IEEE Transactions on Intelligent Transportation Systems,3:182-188.
Koenderink J J.1984.The structure of images [J].Biological Cybernetics.50 (5):363-396.
Lienhart R and Maydt J.2000.An extended set of Haar-like features for rapid object detection//Proceedings of IEEE International Conference on Image Processing.New York,USA:IEEE,9:900-903.
Lindeberg T.1994.Scale-space theory:A basic tool for analysing structures at different scales [J],Journal of Applied Statistics.21(2):224-270.
Lipton A.1999.Local Application of Optic flow to Analyze Rigid Versus Non-rigid Motion [C]//Proceedings of International Conference on Computer Vision Workshop on Frame-Rate Vision.Corfu,Greece:IEEE,http://www.ri.cmu.edu/pubs/pub_2934.html.
Lowe D M.2004.Distinctive image features from scale-invariant keypoints [J].International Journal of Computer Vision,60(2):91-110.
Marill T and Green D.1963.On the e6ectiveness of receptors in recognition systems [J].IEEE Transactions on Information Theory,9:11-17.
Miao G X,Luo Y P,Tian Q M and Tang J X.2006.A filter module used in pedestrian detection system [C]//3~(rd)IFIP Conference on Artificial Intelligence Applications and Innovation.Athens,Greece:Springer Verlag:212-220.
Mikolajczyk K,Schmid C and Zisserman A.2004.Human detection based on a probabilistic assembly of robust part detectors [C]//Proceedings of European Conference on Computer Vision.Prague,Czech Republic:Springer:69-82.
Mohan A,Papageorgiou C and Poggio T.2001.Example-based object detection in images by components.IEEE Transactions on Pattern Analysis and Machine Intelligence [J],23(4):349-361.
Munder S and Gavrila D M.2006.An Experimental Study on Pedestrian Classification [J].Transactions on Pattern Analysis and Machine Intelligence,28(11):1863-1868.
Oren M,Papageorgiou C,Sinha P,Osuna E and Poggio T.1997.Pedestrian detection using wavelet templates [C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.San Juan,Puerto Rico:IEEE:193-199.
Papageorgiou C,Oren M and Poggio T.1998.A general framework for Object Detection [C]//International Conference on Computer Vision.Bombay,India:IEEE:555-562.
Papageorgiou C and Poggio T,1999.Trainable pedestrian detection [J].Image Processing.35-39.
Papageorgiou C and Poggio T.2000.A trainable system for object detection [J].International Journal of Computer Vision,38(1):15-33.
Parra I,Fernandez D,Sotelo M A,Bergasa L M,Revenga P,Nuevo J,Ocana M and Garcia-Garrido M A.2007.Combination of feature extraction methods for SVM pedestrian detection [J].IEEE Transactions on Intelligent Transportation Systems,8(2):292-307.
Pudil P,Novovicova J and Kittler J.1994.Floating search methods in feature selection [J].Pattern Recognition Letters,15:1119-1125.
Sabzmeydani P and Mori G 2007.Detecting Pedestrians by Learning Shapelet Features [C]//IEEE Conference on Computer Vision and Pattern Recognition.Minneapolis,Minnesota,USA:IEEE:1-8.
Siedlecki W and Sklansky J.1988.On automatic feature selection [J].International Journal of Pattern Recognition and Artificial Intelligence,2:197-220.
Ran Y,Zheng Q F,Weiss I,Davis L S,Abd-Almageed W and Zhao L.2005.Pedestrian classification from moving platforms using cyclic motion pattern [C]//Proceedings of International Conference on Image Processing.Genova,Italy:IEEE:2173-2176.
Raskutti B and Kowalczyk A.2004.Extreme Rebalancing for SVMs:a case study [J].Newsletter of the ACM Special Interest Group on Knowledge Discovery and Data Mining,6(1):60-69.
Refregier A.2003.Shapelets:I.A method for image analysis [J].Monthly Notice of the Royal Astronomical Society.338(1):35-47.
Rowley H A,Baluja S and Kanade T.1998.Neural networkbased face detection [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,20(1):23-38.
Schauland S and Kummert A.2007.Implementation and optimization of wavelet and symmetry features for vision-based pedestrian detection [C]//Proceedings of International Conference on Graphics and Visualization in Engineering.Clearwater,Florida,USA:Springer:19-24.
Shashua A,Gdalyahu Y and Hayun G 2004.Pedestrian Detection for Driving Assistance Systems-Single-frame Classification and System Level Performance [C]//Proceedings of IEEE International Conference on Intelligent Vehicles Symposium.Parma,Italy:IEEE:1-6.
Sun H,Hua C Y and Luo Y P.2003.A multi-stage classifier based algorithm of pedestrian detection in night with a near infrared camera in a moving car [C]//Proceedings of the 3~(rd)International Conference on Image and Graphics.Hong Kong,China:Springer:734-741.
Szarvas M,Yoshizawa A,Yamamoto M and Ogata J.2005.Pedestrian detection with convolutional neural networks [C]//Proceedings of IEEE Intelligent Vehicles Symposium.Las Vegas,Nevada:IEEE:224-229.
Tian Q M,Sun H,Luo Y P,Hu D C.2005.Nighttime pedestrian detection with a normal camera using SVM classifier [C]//Proceedings of International Symposium on Neural Networks.
Chongqing,China:Springer:3497:189-194.
Tons M,Doerfler R,Meinecke M M,Obojski M A.2004.Radar sensors and sensor platform used for pedestrian protection in the EC-funded project SAVE-U [C]//IEEE Intelligent Vehicles Symposium.Parma,Italy:IEEE:813-818.
Torresan H,Turgeon B,Ibarra-Castanedo C,Hebert P and Maldague X.2004.Advanced surveillance systems:Combining video and thermal imagery for pedestrian detection [C]//Proceedings of the Society of Photo-optical Instrumentation Engineers.5405:506-515.
Vapnik V.1999.The Nature of Statistical Learning Theory [M].Berlin:Springer-Verlag.
Viola P and Jones M.2001.Rapid Object detection using a boosted cascade of simple features [C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Marriott,Hawaii:IEEE,1:511-518.
Viola P,Jones M and Snow D.2003.Detecting pedestrians using patterns of motion and appearance [C]//Proceedings of International Conference on Computer Vision.Nice,France:Springer:734-741.
Viola P,Jones M and Snow D.2005.Detecting pedestrians using patterns of motion and appearance [J].International Journal of Computer Vision,63(2):153-161.
Wei C X,Cao X B,Xu Y W,Qiao H and Wang F Y.2007.The Treelike Assembly Classifier for Pedestrian Detection [C]//Proceedings of Pacific Asian Workshop on Intelligence and Security Informatics.Chengdu,China:Springer:232-237.
Weiss G M.2004.Mining with Rarity:A Unifying Framework [J].Newsletter of the ACM Special Interest Group on Knowledge Discovery and Data Mining,6(1):7-19.
W(O|¨)hler C,Kressler U and Anlauf J K.2000.Pedestrian recognition by classification of image sequences:global approaches vs.local spatio-temporal processing [C]//Proceedings of 15~(th)International Conference on Pattern Recognition.Barcelona,Spain:IEEE,2:540-544.
Xu F L,Liu X and Fujimura K.2005.Pedestrian detection and tracking with night vision [J].IEEE Transactions on Intelligent Transportation Systems,6:63-71.
Xu Y W,Cao X B,Qiao H.2006.Optical Camera Based Pedestrian Detection in Rainy or Snowy Weather [C]//Proceedings of IEEE International Conference on Fuzzy Systems and Knowledge Discovery.Xi'an,China:Springer,4223:1182-1191.
Zhao L and Thorpe C.2000.Stereo and neural network-based pedestrian detection [J].IEEE Transactions on Intelligent Transportation Systems,1:148-154.
Zhao J and Yao D Y.2006.Occlusion adaptive object tracking based on video image and radar data [C].Proceedings of IEEE International Conference on ITS Telecommunications.Chengdu,China:IEEE:943-946.
Zheng Z H,Wu X Y and Srihari R.2004.Feature Selection for Text Categorization on Imbalanced Data [J].Newsletter of the ACM Special Interest Group on Knowledge Discovery and Data Mining,6(1):80-88.