Performance of optical flow techniques
International Journal of Computer Vision
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to the Special Section on Video Surveillance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Adaptive Tracking to Classify and Monitor Activities in a Site
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
W4: Who? When? Where? What? A Real Time System for Detecting and Tracking People
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
A Real-Time System for Monitoring of Cyclists and Pedestrians
VS '99 Proceedings of the Second IEEE Workshop on Visual Surveillance
Moving Target Classification and Tracking from Real-time Video
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Ghost: A Human Body Part Labeling System Using Silhouettes
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
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This paper presents an example-based learning approach to divide a foreground blob of people into its constituents on a surveillance video camera image. As people tend to walk and interact in groups with other people, occlusions frequently happen in camera images. They are detected in the same foreground image blob and dividing it into image parts of constituting individuals is a prerequisite for high-level vision processing like people tracking and activity understanding. The division is easy for a human observer but difficult in computer vision especially when the image resolution is low. We treat this task as a pattern classification problem by identifying partial outline shape patterns of a foreground blob, which can characterize the position where the blob can be well divided. When a probabilistic neural network was employed to identify the pattern, the network showed over 80% correct recognition rates in experiments.