Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Periodic Motion Detection, Analysis, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Human Detection in Outdoor Scene using Spatio-Temporal Motion Analysis
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Pedestrian Detection in Crowded Scenes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Robust Moving Object Detection at Distance in the Visible Spectrum and Beyond Using A Moving Camera
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Pedestrian detection and tracking in infrared imagery using shape and appearance
Computer Vision and Image Understanding
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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In the paper, we proposed a method for moving human detection in video frames by motion contour matching. Firstly, temporal and spatial difference of frames is calculated and contour pixels are extracted by global thresholding as the basic features. Then, skeleton templates with multiple representative postures are built on these features to represent multiposture human contours. In the detection procedure, a dynamic programming algorithm is adopted to find best global match between the built templates and with extracted contour features. Finally a thresholding method is used to classify a matching result into moving human or negatives. And in the matching process scale problem and interpersonal contour difference are considered. Experiments on real video data prove the effectiveness of the proposed method.