A Bayesian Computer Vision System for Modeling Human Interactions

Nuria Oliver, Barbara Rosario and Alex Pentland


In this paper we describe a real-time computer vision and machine learning system for modeling and recognizing human behaviors in a visual surveillance task. The system is particularly concerned with detecting when interactions between people occur, and classifying the type of interaction. Examples of interesting interaction behaviors include following another person, altering one's path to meet another, and so forth.

Our system combines top-down with bottom-up information in a closed feedback loop, with both components employing a statistical Bayesian approach. We propose and compare two different state-based learning architectures, namely HMMs and CHMMs, for modeling behaviors and interactions. The CHMM model is shown to work much more efficiently and accurately.

Finally, a synthetic agent training system is used to develop a priori models for recognizing human behaviors and interactions. We demonstrate the ability to use these a priori models to accurately classify real human behaviors and interactions with no additional tuning or training.

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Here are some examples of the interactions that our system recognizes as well as the video interpretation of the interacions by the system. On the bottom right corner of each .mpg movie you'll see a label, indicating what is happening in terms of simple actions. Each of these labels corresponds to one state in our CHMMs models.


Meet, talk and continue together

Meet, talk and go on separately

Change direction, meet, talk and go on separately

Last revised Dec98

Nuria Oliver / Microsoft Research