Support vector machines: hype or hallelujah?
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Pedestrian Detection Using Wavelet Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Vision-based approaches for Pedestrian Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Walking pedestrian recognition
IEEE Transactions on Intelligent Transportation Systems
A self-constructing cascade classifier with AdaBoost and SVM for pedestriandetection
Engineering Applications of Artificial Intelligence
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This paper presents two of the most important parts of a vision-based pedestrian detection system: the feature extraction and the classification module. Wavelet-based features and a combination of symmetry and edge density features are extracted from a monochrome image captured by a vehicle-mounted camera and fed into an SVM-classifier, more precisely a modified version of libSVM [1]. For both types of features an optimization approach based on image masks is proposed. In order to weight the impact of classifier results (false negatives are preferred over more false negatives in the case of pedestrian detection) the F-measure is used as statistical measure. An overview on the advantages and drawbacks of the implemented features and the optimization approach is given, based on the results received from tests using pedestrian and non-pedestrian images extracted from video sequences showing urban traffic scenes.