Comparing Shape and Temporal PDMs
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Pocket PC beacons: Wi-Fi based human tracking and following
Proceedings of the 2005 ACM symposium on Applied computing
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Tracking under the nonholonomic constraint using cubic navigation laws
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Face detection using multiple cues
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
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Research at the Computer Vision Laboratory at the University of Maryland has focused on developing algorithms and systems that can look at humans and recognize their activities in near real-time. Our earlier implementation (theW4 system) while quite successful was restricted to applications with a fixed camera. In this paper, we present some recent work that removes this restriction. Such systems are required for machine vision from moving platforms such as robots, intelligent vehicles, and unattended large field of regard cameras with a small field of view. Our approach is based on the use of a deformable shape model for humans coupled with a novel variant of the Condensation algorithm that uses quasi-random sampling for efficiency. This allows the use of simple motion models, which results in algorithm robustness, enabling us to handle unknown camera/human motion with unrestricted camera viewing angles. We present the details of our human tracking algorithms and some examples from pedestrian tracking and automated surveillance.