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Abstract-Static cameras are pervasive in a variety of environments. However it remains a challenging problem to extract and reason about high-level features from real-time and continuous observation of an environment. In this paper, we present CAMEO, the Camera Assisted Meeting Event Observer, which is a physical awareness system designed for use by an agent-based electronic assistant. CAMEO is an inexpensive high-resolution omnidirectional vision system designed to be used in meeting environments. The multiple camera design achieves the desired high image resolution and lower cost that can be achieved when compared to traditional omnicameras that make use of a single camera and mirror solution.

Abstract-We have designed a physical awareness system called CAMEO, the Camera Assisted Meeting Event Observer, which consists of a multi-camera omnidirectional vision system designed to be used in meeting environments. CAMEO is designed to monitor the activities of people in meetings so that it can generate a semantically-indexed summary of what occurred in the meeting. In this paper, we describe CAMEO's fast people detection and tracking module. This module makes use of a combination of frame differencing, face detection, and adaptive color blob tracking based on mean shift analysis to detect and track people in the panoramic image. We describe this algorithm and present experimental results from captured meeting logs.