Pedestrian Protection Systems: Issues, Survey, and Challenges <s> D. Classifiers for Pedestrian Discrimination <s> In this paper, we present a real-time pedestrian detection system that uses a pair of moving cameras to detect both stationary and moving pedestrians in crowded environments. This is achieved through stereo-based segmentation and neural network-based recognition. Stereo-based segmentation allows us to extract objects from a changing background; neural network-based recognition allows us to identify pedestrians in various poses, shapes, sizes, clothing, occlusion status. The experiments on a large number of urban street scenes demonstrate the feasibility of the approach in terms of pedestrian detection rate and frame processing rate. <s> BIB001 </s> Pedestrian Protection Systems: Issues, Survey, and Challenges <s> D. Classifiers for Pedestrian Discrimination <s> This paper presents a method for pedestrian detection and tracking using a night vision video camera installed on the vehicle. To deal with the nonrigid nature of human appearance on the road, a two-step detection/tracking method is proposed. The detection phase is performed by a support vector machine (SVM) with size-normalized pedestrian candidates and the tracking phase is a combination of Kalman filter prediction and mean shift tracking. The detection phase is further strengthened by information obtained by a road detection module that provides key information for pedestrian validation. Experimental comparisons have been carried out on gray-scale SVM recognition vs. binary SVM recognition and entire body detection vs. upper body detection. <s> BIB002 </s> Pedestrian Protection Systems: Issues, Survey, and Challenges <s> D. Classifiers for Pedestrian Discrimination <s> This paper describes a pedestrian detection system that integrates image intensity information with motion information. We use a detection style algorithm that scans a detector over two consecutive frames of a video sequence. The detector is trained (using AdaBoost) to take advantage of both motion and appearance information to detect a walking person. Past approaches have built detectors based on appearance information, but ours is the first to combine both sources of information in a single detector. The implementation described runs at about 4 frames/second, detects pedestrians at very small scales (as small as 20/spl times/15 pixels), and has a very low false positive rate. Our approach builds on the detection work of Viola and Jones. Novel contributions of this paper include: i) development of a representation of image motion which is extremely efficient, and ii) implementation of a state of the art pedestrian detection system which operates on low resolution images under difficult conditions (such as rain and snow). <s> BIB003 </s> Pedestrian Protection Systems: Issues, Survey, and Challenges <s> D. Classifiers for Pedestrian Discrimination <s> This paper presents a general, trainable system for object detection in unconstrained, cluttered scenes. The system derives much of its power from a representation that describes an object class in terms of an overcomplete dictionary of local, oriented, multiscale intensity differences between adjacent regions, efficiently computable as a Haar wavelet transform. This example-based learning approach implicitly derives a model of an object class by training a support vector machine classifier using a large set of positive and negative examples. We present results on face, people, and car detection tasks using the same architecture. In addition, we quantify how the representation affects detection performance by considering several alternate representations including pixels and principal components. We also describe a real-time application of our person detection system as part of a driver assistance system. <s> BIB004 </s> Pedestrian Protection Systems: Issues, Survey, and Challenges <s> D. Classifiers for Pedestrian Discrimination <s> This paper presents the results of the first large-scale field tests on vision-based pedestrian protection from a moving vehicle. Our PROTECTOR system combines pedestrian detection, trajectory estimation, risk assessment and driver warning. The paper pursues a "system approach" related to the detection component. An optimization scheme models the system as a succession of individual modules and finds a good overall parameter setting by combining individual ROCs using a convex-hull technique. On the experimental side, we present a methodology for the validation of the pedestrian detection performance in an actual vehicle setting. We hope this test methodology to contribute towards the establishment of benchmark testing, enabling this application to mature. We validate the PROTECTOR system using the proposed methodology and present interesting quantitative results based on tens of thousands of images from hours of driving. Although results are promising, more research is needed before such systems can be placed at the hands of ordinary vehicle drivers. <s> BIB005 </s> Pedestrian Protection Systems: Issues, Survey, and Challenges <s> D. Classifiers for Pedestrian Discrimination <s> Vehicle warning and control systems are the key component of ITS. Pedestrian detection is an important research content of vehicle active safety. The central idea behind such pedestrian safety systems is to protect the pedestrian from injuries. In this paper, we address the problem of pedestrian represent and detection where the motion cue is not used. Inspired by the work proposed by Zehang Sun [2004], we proposed a pedestrian feature representation approach based on sparse Gabor filters (SGF) learning from examples. In the phase of pedestrian detection, we used support vector machine to detect the pedestrian. Promising results demonstrate the potential of the proposed framework. <s> BIB006 </s> Pedestrian Protection Systems: Issues, Survey, and Challenges <s> D. Classifiers for Pedestrian Discrimination <s> This paper presents a novel pedestrian detection method based on the use of a convolutional neural network (CNN) classifier. Our method achieves high accuracy by automatically optimizing the feature representation to the detection task and regularizing the neural network. We evaluate the proposed method on a difficult database containing pedestrians in a city environment with no restrictions on pose, action, background and lighting conditions. The false positive rate (FPR) of the proposed CNN classifier is less than 1/5-th of the FPR of a support vector machine (SVM) classifier using Haar-wavelet features when the detection rate is 90%. The accuracy of the SVM classifier using the features learnt by the CNN is equivalent to the accuracy of the CNN, confirming the importance of automatically optimized features. The computational demand of the CNN classifier is, however, more than an order of magnitude lower than that of the SVM, irrespective of the type of features used. <s> BIB007 </s> Pedestrian Protection Systems: Issues, Survey, and Challenges <s> D. Classifiers for Pedestrian Discrimination <s> Thispaperpresents acomplete methodforpedes- trian detection applied toinfrared images. First, westudy an imagedescriptor basedonhistograms oforiented gradients (HOG),associated witha Support Vector Machine(SVM) classifier andevaluate itsefficiency. Afterhaving tunedthe HOG descriptor andtheclassifier, weinclude this methodin acomplete system, whichdeals withstereo infrared images. Thisapproach gives goodresults forwindowclassification, andapreliminary testapplied onavideosequence proves thatthis approach isverypromising. I.INTRODUCTION Since thelast fewyears now,thedevelopment ofdriving assistance systems hasbeenveryactive inorder toin- crease thevehicle anditsenvironment safety. Atthe present time, themainobjective inthisdomainisto provide thedrivers withsomeinformation concerning its environment andanypotential hazard. Oneamongall useful information isthedetection andlocalization ofa pedestrian infront ofavehicle. Thisproblem ofdetecting pedestrians isaverydifficult problemthathasessentially beenaddressed using vision sensors, imageprocessing andpattern recognition techniques. Inparticular, detecting pedestrians thanks to images isacomplex challenge duetotheir appearance andposevariability. Inthecontext ofdaylight vision, several approaches havebeenproposed andarebased on different imageprocessing techniques ormachine learning (9), (5), (12). Recently, owingtothedevelopment oflow-cost infrared cameras, night vision systems havegained moreand moreinterest, thusincreasing theneedofautomatic detection ofpedestrians atnight. Thisproblem of detecting pedestrians frominfrared images hasbeen investigated byvarious research teamsinthelast years. Themainmethodology isbasedon extracting cues (symmetry, shape-independent <s> BIB008 </s> Pedestrian Protection Systems: Issues, Survey, and Challenges <s> D. Classifiers for Pedestrian Discrimination <s> In a pedestrian detection system, the most critical requirement is to quickly and reliably determine whether a candidate region contains a pedestrian. It is essential to design an effective classifier for pedestrian detection. Until now, most of the existing pedestrian detection systems only adopt a single and non-cascaded classifier. However, since the scene is complex and the candidate regions are too many (in our experiments, there are more than 40,000 candidate regions); it is difficult to make the recognition both accurate and fast with such a non-cascaded classifier. In this paper, we present a cascaded classifier for pedestrian detection. The cascaded classifier combines a statistical learning classifier and a support vector machine classifier. The statistical learning classifier is used to select preliminary candidates, and then the support vector machine classifier is applied to do a further acknowledgement. This kind of cascaded architecture can take both advantages of the two classifiers, so the detecting rate and detecting speed can be balanced. Experimental results illustrate that the cascaded classifier is effective for a real-time detection <s> BIB009 </s> Pedestrian Protection Systems: Issues, Survey, and Challenges <s> D. Classifiers for Pedestrian Discrimination <s> Detecting people in images is key for several important application domains in computer vision. This paper presents an in-depth experimental study on pedestrian classification; multiple feature-classifier combinations are examined with respect to their ROC performance and efficiency. We investigate global versus local and adaptive versus nonadaptive features, as exemplified by PCA coefficients, Haar wavelets, and local receptive fields (LRFs). In terms of classifiers, we consider the popular support vector machines (SVMs), feedforward neural networks, and k-nearest neighbor classifier. Experiments are performed on a large data set consisting of 4,000 pedestrian and more than 25,000 nonpedestrian (labeled) images captured in outdoor urban environments. Statistically meaningful results are obtained by analyzing performance variances caused by varying training and test sets. Furthermore, we investigate how classification performance and training sample size are correlated. Sample size is adjusted by increasing the number of manually labeled training data or by employing automatic bootstrapping or cascade techniques. Our experiments show that the novel combination of SVMs with LRF features performs best. A boosted cascade of Haar wavelets can, however, reach quite competitive results, at a fraction of computational cost. The data set used in this paper is made public, establishing a benchmark for this important problem <s> BIB010 </s> Pedestrian Protection Systems: Issues, Survey, and Challenges <s> D. Classifiers for Pedestrian Discrimination <s> This paper describes a comprehensive combination of feature extraction methods for vision-based pedestrian detection in the framework of intelligent transportation systems. The basic components of pedestrians are first located in the image and then combined with a SVM-based classifier. This poses the problem of pedestrian detection in real, cluttered road images. Candidate pedestrians are located using a subtractive clustering attention mechanism based on stereo vision. A by-components learning approach is proposed in order to better deal with pedestrians variability, illumination conditions, partial occlusions, and rotations. Extensive comparisons have been carried out using different feature extraction methods, as a key to image understanding in real traffic conditions. A database containing thousands of pedestrian samples extracted from real traffic images has been created for learning purposes, either at daytime and nighttime. The results achieved up to date show interesting conclusions that suggest a combination of feature extraction methods as an essential clue for enhanced detection performance <s> BIB011
Various types of classifiers have been employed to distinguish pedestrians from nonpedestrian objects. The input to the classifier is a vector of raw pixel values or features extracted from them, and the output is the decision showing whether the object is detected or not. In many cases, the confidence values are also returned. The classifiers are usually trained using a number of positive and negative examples to determine the decision boundary between them. After training, the classifier processes unknown samples and decides the presence or absence of the object based on which side of the decision boundary the feature vector lies. Some of the classifiers that are used for pedestrian detection are the following: support vector machines (SVMs), various types of neural networks, and statistical learning classifiers such as AdaBoost, as shown in Table VI . SVMs form a hyperplane decision boundary by maximizing the "margin," i.e., the separation between the nearest samples on two sides of the boundary. They are used for pedestrian detection in conjunction with features such as Haar wavelets BIB004 , sparse Gabor filters BIB006 , image regions corresponding to hot spots in thermal IR images BIB002 , and histograms of oriented gradients BIB008 . A neural network can obtain highly nonlinear boundaries between classes based on the training samples given to it from each class and, therefore, can account for large-shape variations BIB001 . In BIB001 , neural networks are used on intensity gradientbased features to recognize pedestrians. In BIB005 , a neural network with local receptive fields (LRFs) and shared weights is used to perform texture-based verification of pedestrians. LRF and shared weights reduce the degrees of freedom of the neural network and therefore allow training to be carried out with fewer samples. In BIB007 , a convolutional neural network (CNN) classifier, which is similar to the LRF BIB005 , is used to automatically learn appropriate features and obtain improved detection performance. A fivefold improvement in false alarm rate is claimed over the SVM classifier which used Haarwavelet features. AdaBoost is a method to combine several weak classifiers into a strong classifier using a weighted sum whose weights are iteratively learned from the samples misclassified by the current classifier. This is often used in the form of a cascade of classifiers of increasing complexity. In BIB003 , AdaBoost and cascading are used with appearance and motion features to identify pedestrians. Cascade classifiers are also used in BIB009 , where a statistical learning classifier quickly identifies candidates and the more complex SVM-based classifier checks for genuine targets. In BIB010 , an elaborate experimental study of pedestrian detection is performed, comparing the use of various features (PCA coefficients, Haar wavelets, and LRFs) and classifiers (feedforward neural network, SVM, and the baseline of k-nearest neighbor). It is observed that LRFs combined with SVM give best performance in terms of rates of detection and false alarms. The publication also establishes a public benchmark dataset for pedestrian classification, which is downloadable from the web. In BIB011 , a two-stage classifier based on SVM is used to detect pedestrians. The first stage is trained to detect parts of the body such as head, torso, and legs. The second stage integrates the 