Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Construction of 2D Shape Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Pedestrian Detection from a Moving Vehicle
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
A Linear Time Algorithm for Computing the Euclidean Distance Transform in Arbitrary Dimensions
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Learning Shape Models from Examples
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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We present an approach to vision-based person detection in robotic applications that integrates top down template matching with bottom up classifiers. We detect components of the human silhouette, such as torso and legs; this approach provides greater invariance than monolithic methods to the wide variety of poses a person can be in. We detect borders on each image, then apply a distance transform, and then match templates at different scales. This matching process generates a focus of attention (candidate people) that are later confirmed using a trained Support Vector Machine (SVM) classifier. Our results show that this method is both fast and precise and directly applicable in robotic architectures.