Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovery and Segmentation of Activities in Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to the Special Section on Video Surveillance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ghost3D: Detecting Body Posture and Parts Using Stereo
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
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)
Real-time Human Motion Analysis by Image Skeletonization
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
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People are difficult targets to process in video surveillance and monitoring (VSAM) because of small size and non-rigid motion. In this paper, we address neural network application to people tracking for VSAM. A feedforward multilayer perceptron network (FMPN) is employed for the tracking in low-resolution image sequences using position, shape, and color cues. When multiple people are partly occluded by themselves, the foreground image patch of the people group detected is divided into individuals using another FMPN. This network incorporates three different techniques relying on a line connecting top pixels of the binary foreground image, the vertical projection of the binary foreground image, and pixel value variances of divided regions. The use of neural networks provides efficient tracking in real outdoor situations particularly where the detailed visual information of people is unavailable due mainly to low image resolution.