Prabhu Natarajan

Multi-Camera Coordination and Control in Surveillance Systems: A Survey

The use of multiple heterogeneous cameras is becoming more common in todays surveillance systems. In order to perform surveillance tasks, effective coordination and control in multi-camera systems is very important, and is catching significant research attention these days. This survey aims to provide researchers with a state-of-the-art overview of various techniques for multi-camera coordination and control (MC3) that have been adopted in surveillance systems. The existing literature on MC3 is presented through several classifications based on the applicable architectures, frameworks and the associated surveillance tasks. Finally, a discussion on the open problems in surveillance area that can be solved effectively using MC3 and the future directions in MC3 research is presented

Scalable Decision-Theoretic Coordination and Control for Real-time Active Multi-Camera Surveillance

This paper presents an overview of our novel decision-theoretic multi-agent approach for controlling and coordinating multiple active cameras in surveillance. In this approach, a surveillance task is modeled as a stochastic optimization problem, where the active cameras are controlled and coordinated to achieve the desired surveillance goal in presence of uncertainties. We enumerate the practical issues in active camera surveillance and discuss how these issues are addressed in our decision-theoretic approach. We focus on two novel surveillance tasks: maximize the number of targets observed in active cameras with guaranteed image resolution and to improve the fairness in observation of multiple targets. We discuss the overview of our novel decision-theoretic frameworks: Markov Decision Process and Partially Observable Markov Decision Process frameworks for coordinating active cameras in uncertain and partially occluded environments.

Multi-camera Skype: Enhancing the Quality of Experience of Video Conferencing

We propose a novel approach towards real-time control, selection and transmission of the best view of human faces in Skype video conferencing. Our goal is to improve the Quality-of-Experience (QoE) of current video conferencing services by incorporating real-time multi-camera control and selection mechanism. Traditional 3D viewpoint selection algorithms rely on complex 3D-model computation and are not applicable for real-time applications. We define a new image-based metric, Viewpoint Saliency (VS), for evaluating the quality of views of human subject and a centralized multi-camera control mechanism to track and select the best view of human.