This paper presents a video acquisition system that can leam automatic video capture from human's camera operations. Unlike a predefined camera control system, this system can easily adapt to its environment changes with users' help. By collecting users' camera-control operations under various environments, the control system can learn video captnre from humaq and use these learned skills to operate its cameras when remote viewers don't, won't, or can't operate the system. Moreover, this system allows remote viewers to control their own virtual cameras instead of watching the same video produced by a human operator or a fully automatic system. The online learning algorithm and the camera management algorithm are demonstrated using field data.

This paper presents an information-driven online video composition system. The composition work handled by the system includes dynamically setting multiple pan/tilt/zoom (PTZ) cameras to proper poses and selecting the best close-up view for passive viewers. The main idea of the composition system is to maximize captured video information with limited cameras. Unlike video composition based on heuristic rules, our video composition is formulated as a process of minimizing distortions between ideal signals (i.e. signals with infinite spatial-temporal resolution) and displayed signals. The formulation is consistent with many well-known empirical approaches widely used in previous systems and may provide analytical explanations to those approaches. Moreover, it provides a novel approach for studying video composition tasks systematically.The composition system allows each user to select a personal closeup view. It manages PTZ cameras and a video switcher based on both signal characteristics and users' view selections. Additionally, it can automate the video composition process based on past users' view-selections when immediate selections are not available. We demonstrate the performance of this system with real meetings.