Abstract
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.
This research was supported by the National Business Center (NBC) of the Department of the Interior (DOI) under a subcontract from SRI International. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, by the NBC, DOI, SRI, or the US Government.
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Rybski, P.E., Veloso, M.M. (2005). Robust Real-Time Human Activity Recognition from Tracked Face Displacements. In: Bento, C., Cardoso, A., Dias, G. (eds) Progress in Artificial Intelligence. EPIA 2005. Lecture Notes in Computer Science(), vol 3808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595014_9
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DOI: https://doi.org/10.1007/11595014_9
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