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.
Task Scheduling in Large Camera Networks
Asian Conference on Computer Vision ; 2007 ; Tokyo, Japan November 18, 2007 - November 22, 2007
2007-01-01
11 pages
Article/Chapter (Book)
Electronic Resource
English
Schedule Problem , Dynamic Programming , Source Node , Greedy Algorithm , Directed Acyclic Graph Computer Science , Image Processing and Computer Vision , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Artificial Intelligence , Biometrics , Algorithm Analysis and Problem Complexity
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