Unified Multi-Camera Detection and Tracking Using Region-Matching

Authors:
Anurag Mittal;Larry Davis
Affiliations:
-;-
Venue:
WOMOT '01 Proceedings of the IEEE Workshop on Multi-Object Tracking (WOMOT'01)
Year:
2001

Citing 0
Cited 13

M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene

International Journal of Computer Vision
M2Tracker: A Multi-view Approach to Segmenting and Tracking People in a Cluttered Scene Using Region-Based Stereo

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
An integrated multi-modal sensor network for video surveillance

Proceedings of the third ACM international workshop on Video surveillance & sensor networks
A system for automatic face obscuration for privacy purposes

Pattern Recognition Letters
A system for automatic face obscuration for privacy purposes

Pattern Recognition Letters - Special issue on vision for crime detection and prevention
Distributed Bayesian multiple-target tracking in crowded environments using multiple collaborative cameras

EURASIP Journal on Applied Signal Processing
HECOL: Homography and epipolar-based consistent labeling for outdoor park surveillance

Computer Vision and Image Understanding
Object matching in disjoint cameras using a color transfer approach

Machine Vision and Applications
System and software architectures of distributed smart cameras

ACM Transactions on Embedded Computing Systems (TECS)
Tracking in a Dense Crowd Using Multiple Cameras

International Journal of Computer Vision
A novel multi-object detection method in complex scene using synthetic aperture imaging

Pattern Recognition
Consistent labeling for multi-camera object tracking

ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Localizing people in multi-view environment using height map reconstruction in real-time

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

Abstract: We describe an algorithm for detecting and tracking multiple people in a cluttered scene using multiple synchronized cameras located far away from each other. This camera arrangement results in multiple wide-baseline camera systems. We segment each image, and then, for each pair we compare regions across the views along epipolar lines. The centers of the matching segments are then back-projected to identify 3D points in the scene potentially corresponding to people. These 3D points are then projected onto the ground plane. The results from these wide-baseline camera systems are then combined using a scheme that rejects outliers and gives very robust estimates of the 2D locations of the people. These estimates are then used to track people across time. We have found that the algorithm works quite well in practice in scenes containing multiple people even when they occlude each other in every camera view.