LAUSR

The decreasing cost and size of video sensors has led to camera networks becoming pervasive in our lives. However, the ability to analyze these images effectively is very much a function of the quality of the acquired images. In this paper, we consider the problem of automatically controlling the fields of view of individual pan–tilt–zoom (PTZ) cameras in a camera network leading to improved situation awareness (e.g., where and what are the critical targets and events) in a region of interest. The network of cameras attempts to observe the entire region of interest at some minimum resolution while opportunistically acquiring high resolution images of critical events in real time. Since many activities involve groups of people interacting, an important decision that the network needs to make is whether to focus on individuals or groups of them. This is achieved by understanding the performance of video analysis tasks and designing camera control strategies to improve a metric that quantifies the quality of the source imagery. Optimization strategies, along with a distributed implementation, are proposed, and their theoretical properties analyzed. The proposed methods bring together computer vision and network control ideas. The performance of the proposed methodologies discussed herein has been evaluated on a real-life wireless network of PTZ capable cameras.