Name
Abstract | ||
|---|---|---|
ABSTRACTVehicle trajectories provide essential information to understand the urban mobility and benefit a wide range of urban applications. State-of-the-art solutions for vehicle sensing may not build accurate and complete knowledge of all vehicle trajectories. In order to fill the gap, this paper proposes VeTrac, a comprehensive system that employs widely deployed traffic cameras as a sensing network to trace vehicle movements and reconstruct their trajectories in a large scale. VeTrac fuses mobility correlation and vision-based analysis to reduce uncertainties in identifying vehicles. A graph convolution process is employed to maintain the identity consistency across different camera observations, and a self-training process is invoked when aligning with the urban road network to reconstruct vehicle trajectories with confidence. Extensive experiments with real-world data input of over 7 million vehicle snapshots from over one thousand traffic cameras demonstrate that VeTrac achieves 98% accuracy for simple expressway scenario and 89% accuracy for complex urban environment. The achieved accuracy outperforms alternative solutions by 32% for expressway scenario and by 59% for complex urban environment. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3447993.3448617 | MOBICOM |
Keywords | DocType | Citations |
Trajectory Reconstruction, Camera Sensing Network, Vehicle Mobility, Identity Uncertainty | Conference | 1 |
PageRank | References | Authors |
0.35 | 27 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ping Tong | 1 | 1 | 0.69 |
| Mo Li | 2 | 2324 | 106.92 |
| Mo Li | 3 | 2324 | 106.92 |
| Jianqiang Huang | 4 | 55 | 19.18 |
| Xian-Sheng Hua | 5 | 6566 | 328.17 |