Paper Info
Title
Robust People Detection and Tracking in a Multi-Camera Indoor Visual Surveillance System
Abstract
In this paper we describe the analysis component of an indoor, real-time, multi-camera surveillance system. The analysis includes: (1) a novel feature-level foreground segmentation method which achieves efficient and reliable segmentation results even under complex conditions, (2) an efficient greedy search based approach for tracking multiple people through occlusion, and (3) a method for multi-camera handoff that associates individual trajectories in adjacent cameras. The analysis is used for an 18 camera surveillance system that has been running continuously in an indoor business over the past several months. Our experiments demonstrate that the processing method for people detection and tracking across multiple cameras is fast and robust.
Year
DOI
Venue
2007
10.1109/ICME.2007.4284740
ICME
Keywords
Field
DocType
multicamera indoor visual surveillance,image segmentation,computer graphics,realtime surveillance,robust people detection,camera surveillance,multicamera handoff,people tracking,feature-level foreground segmentation,occlusion,greedy search,video surveillance,real time,robustness,real time systems,face detection,business,pixel,elevators
Computer vision,Segmentation,Computer science,Tracking system,Image segmentation,Greedy algorithm,Robustness (computer science),Artificial intelligence,Pixel,Face detection,Computer graphics
Conference
ISBN
Citations 
PageRank 
1-4244-1017-7
8
0.76
References 
Authors
9
4
Name
Order
Citations
PageRank
Tao Yang121714.48
Francine Chen21218153.96
Don Kimber336543.84
Jim Vaughan4698.52