Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Tracking non-rigid, moving objects based on color cluster flow
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Pedestrian Detection Using Wavelet Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Camera-Based System for Tracking People in Real Time
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Robust Real-Time Periodic Motion Detection, Analysis, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Autonomous Driving Goes Downtown
IEEE Intelligent Systems
Pedestrian Detection from a Moving Vehicle
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Hand Motion Gesture Frequency Properties and Multimodal Discourse Analysis
International Journal of Computer Vision
Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle
International Journal of Computer Vision
EURASIP Journal on Applied Signal Processing
Machine Vision and Applications
Context Information for Human Behavior Analysis and Prediction
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Robust pedestrian detection and tracking in crowded scenes
Image and Vision Computing
Human motion recognition using Isomap and dynamic time warping
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Automatic multimodal descriptors of rhythmic body movement
Proceedings of the 15th ACM on International conference on multimodal interaction
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In this paper we present an algorithm for recognizing walking pedestrians in sequences of color images taken from a moving camera. The recognition is based on the characteristic motion of the legs of a pedestrian walking parallel to the image plane. Each image is segmented into region-like image parts by clustering pixels in a combined color/position feature space. The proposed clustering technique implies matching of corresponding clusters in consecutive frames and therefore allows clusters to be tracked over a sequence of images. Based on the observation of clusters over time a two-stage classifier extracts those clusters which most likely represent the legs of pedestrians. A fast polynomial classifier performs a rough preselection of clusters by evaluating temporal changes of a shape-dependent cluster feature. The final classification is done by a time delay neural network (TDNN) with spatio-temporal receptive fields.