Integrated Person Tracking Using Stereo, Color, and Pattern Detection
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Gesture Modeling and Recognition Using Finite State Machines
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
An unsupervised approach to activity recognition and segmentation based on object-use fingerprints
Data & Knowledge Engineering
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We are interested in the challenging scientific pursuit of how to characterize human activities in any formal meeting situation by tracking people’s positions with a computer vision system. We present a human activity recognition algorithm that works within the framework of CAMEO (the Camera Assisted Meeting Event Observer), a panoramic vision system designed to operate in real-time and in uncalibrated environments. Human activity is difficult to characterize within the constraints that the CAMEO must operate, including uncalibrated deployment and unmodeled occlusions. This paper describes these challenges and how we address them by identifying invariant features and robust activity models. We present experimental results of our recognizer correctly classifying person data.