Travel-time estimation of traffic flow is an important problem with critical implications for traffic congestion analysis. We developed techniques for using intersection videos to identify vehicle trajectories across multiple cameras and analyze corridor travel time. Our approach consists of (1) multi-object single-camera tracking, (2) vehicle re-identification among different cameras, (3) multi-object multi-camera tracking, and (4) travel-time estimation. We evaluated the proposed framework on real intersections in Florida with pan and fisheye cameras. The experimental results demonstrate the viability and effectiveness of our method.
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Adaptive multi-vehicle motion counting
Counting multi-vehicle motions via traffic cameras in urban areas is crucial for smart cities. Even though several frameworks have been proposed in this task, there is no prior work focusing on the highly common, dense and size-variant vehicles such as motorcycles. In this paper, we propose a novel framework for vehicle motion counting with adaptive label-independent tracking and counting modules that processes 12 frames per second. Our framework adapts hyperparameters for multi-vehicle tracking and properly works in complex traffic conditions, especially invariant to camera perspectives. We achieved the competitive results in terms of root-mean-square error and runtime performance.
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- Award ID(s):
- 2025234
- NSF-PAR ID:
- 10330090
- Date Published:
- Journal Name:
- Signal, Image and Video Processing
- ISSN:
- 1863-1703
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s11760-022-02184-5
