Anurag Mittal

Vision systems are increasingly being deployed to perform complex surveillance tasks. While improved algorithms are being developed to perform these tasks, it is also important that data suitable for these algorithms be acquired - a non-trivial task in a dynamic and crowded scene viewed by multiple PTZ cameras. In this paper, we describe a real-time multi-camera system that collects images and videos of moving objects in such scenes, subject to task constraints. The system constructs "task visibility intervals" that contain information about what can be sensed in future time intervals. Constructing these intervals requires prediction of future object motion and consideration of several factors such as object occlusion and camera control parameters. Such intervals can also be combined to form multi-task intervals, during which a single camera can collect videos suitable for multiple tasks simultaneously. Experimental results are provided to illustrate the system capabilities in constructing such task visibility intervals, followed by scheduling them using a greedy algorithm. © Springer-Verlag 2006.

Camera networks are increasingly being deployed for security. In most of these camera networks, video sequences are captured, transmitted and archived continuously from all cameras, creating enormous stress on available transmission bandwidth, storage space and computing facilities. We describe an intelligent control system for scheduling Pan-Tilt-Zoom cameras to capture video only when task-specific requirements can be satisfied. These videos are collected in real time during predicted temporal "windows of opportunity". We present a scalable algorithm that constructs schedules in which multiple tasks can possibly be satisfied simultaneously by a given camera. We describe two scheduling algorithms: a greedy algorithm and another based on Dynamic Programming (DP). We analyze their approximation factors and present simulations that show that the DP method is advantageous for large camera networks in terms of task coverage. Results from a prototype real time active camera system however reveal that the greedy algorithm performs faster than the DP algorithm, making it more suitable for a real time system. The prototype system, built using existing low-level vision algorithms, also illustrates the applicability of our algorithms. © Springer-Verlag Berlin Heidelberg 2007.