Paper Info
Title
Tracking Of Multiple Objects Across Multiple Cameras With Overlapping And Non-Overlapping Views
Abstract
In this paper, we propose a fully automated approach for tracking of multiple objects across multiple cameras with overlapping and non-overlapping views in a unified framework without initial training. For single camera cases, Kalman filter and adaptive particle sampling are integrated for multiple objects tracking. When extended to multiple cameras cases, the relations between adjacent cameras are learned systematically by using image registration techniques for consistent handoff of tracking-object labels across cameras. In addition, object appearance measurement is employed to validate the labeling results. Experimental results demonstrate the performance of our approach on real video sequences for cameras with overlapping and non-overlapping views.
Year
DOI
Venue
2009
10.1109/ISCAS.2009.5117941
ISCAS: 2009 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOLS 1-5
Keywords
Field
DocType
histograms,kalman filter,computer science,kalman filters,image registration,adaptive filters,tracking,labeling,computer networks,network topology
Object detection,Computer vision,Histogram,Computer science,Network topology,Kalman filter,Artificial intelligence,Sampling (statistics),Adaptive filter,Image registration,Handover
Conference
Citations 
PageRank 
References 
5
0.44
10
Authors
3
Name
Order
Citations
PageRank
Liangjia Zhu1929.07
Jenq-Neng Hwang21675206.57
Hsu-Yung Cheng324323.56