CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
A Unified Framework for Tracking through Occlusions and across Sensor Gaps
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Active vision in robotic systems: A survey of recent developments
International Journal of Robotics Research
Online control of active camera networks for computer vision tasks
ACM Transactions on Sensor Networks (TOSN)
Hi-index | 0.00 |
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.