Name
Title | ||
|---|---|---|
Multiple-Target Tracking for Crossroad Traffic Utilizing Modified Probabilistic Data Association |
Abstract | ||
|---|---|---|
A multiple-target tracking system aimed at analyzing crossroad traffic systematically is proposed in this paper. The proposed mechanism is based on Kalman filtering and modified probabilistic data association. Unlike traditional Kalman filtering tracking, the proposed mechanism constructs candidate measurement lists by matching the sizes of the measurements and the targets first. When the sizes do not match, object matching within a limited area is performed. Also, we modify the classical Probabilistic Data Association method to enhance its performance and make it more suitable for vision-based systems. The proposed mechanism, which can serve as the foundation for automatic traffic event detection, can solve the occlusion problems effectively without incurring too much computational complexity. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICASSP.2007.366059 | ICASSP |
Keywords | Field | DocType |
Kalman filters,computational complexity,computer vision,image matching,probability,road traffic,target tracking,video signal processing,Kalman filtering,automatic traffic event detection,computational complexity,crossroad traffic,modified probabilistic data association,multiple-target tracking,object matching,occlusion problems,vision-based systems,crossroad traffic analysis,intelligent systems,tracking,video signal processing | Data mining,Intelligent decision support system,Computer science,Tracking system,Artificial intelligence,Probabilistic logic,Matched filter,Pattern recognition,Filter (signal processing),Kalman filter,Intelligent transportation system,Machine learning,Computational complexity theory | Conference |
Volume | ISSN | Citations |
1 | 1520-6149 | 12 |
PageRank | References | Authors |
0.84 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hsu-Yung Cheng | 1 | 243 | 23.56 |
| Jenq-Neng Hwang | 2 | 1675 | 206.57 |