Robust Real-Time Human Activity Recognition from Tracked Face Displacements

Paul Rybski and Manuela Veloso
Proceedings of the 12th Portuguese Conference on Artificial Intelligence, EPIA 2005, Lecture Notes in Artificial Intelligence 3808, December, 2005, pp. 87-98.


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Abstract
We are interested in the challenging scientific pursuit of how to characterize human activities in any formal meeting situation by tracking people? 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.

Notes
Number of pages: 12

Text Reference
Paul Rybski and Manuela Veloso, "Robust Real-Time Human Activity Recognition from Tracked Face Displacements," Proceedings of the 12th Portuguese Conference on Artificial Intelligence, EPIA 2005, Lecture Notes in Artificial Intelligence 3808, December, 2005, pp. 87-98.

BibTeX Reference
@inproceedings{Rybski_2005_5996,
   author = "Paul Rybski and Manuela Veloso",
   editor = "C. Bento, A. Cardoso and G. Dias",
   title = "Robust Real-Time Human Activity Recognition from Tracked Face Displacements",
   booktitle = "Proceedings of the 12th Portuguese Conference on Artificial Intelligence, EPIA 2005, Lecture Notes in Artificial Intelligence 3808",
   pages = "87-98",
   publisher = "LNAI Springer",
   month = "December",
   year = "2005",
}