ArticleSensors (Basel, Switzerland)2020
Vehicle Spatial Distribution and 3D Trajectory Extraction Algorithm in a Cross-Camera Traffic Scene.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Identification and Tracking of Vehicles between Multiple Cameras on Bridges Using a YOLOv4 and OSNet-Based Method.Sensors (Basel, Switzerland) · 2023Article
- An Accurate and Convenient Method of Vehicle Spatiotemporal Distribution Recognition Based on Computer Vision.Sensors (Basel, Switzerland) · 2022Article
- 3D Vehicle Trajectory Extraction Using DCNN in an Overlapping Multi-Camera Crossroad Scene.Sensors (Basel, Switzerland) · 2021Article
- Design and Implementation of Intelligent Agent Training Systems for Virtual Vehicles.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
The three-dimensional trajectory data of vehicles have important practical meaning for traffic behavior analysis. To solve the problems of narrow visual angle in single-camera scenes and lack of continuous trajectories in 3D space by current cross-camera trajectory extraction methods, we propose an algorithm of vehicle spatial distribution and 3D trajectory extraction in this paper. First, a panoramic image of a road with spatial information is generated based on camera calibration, which is used to convert cross-camera perspectives into 3D physical space. Then, we choose YOLOv4 to obtain 2D bounding boxes of vehicles in cross-camera scenes. Based on the above information, 3D bounding boxes around vehicles are built with geometric constraints which are used to obtain projection centroids of vehicles. Finally, by calculating the spatial distribution of projection centroids in the panoramic image, 3D trajectories of vehicles are extracted. The experimental results indicate that our algorithm can effectively complete vehicle spatial distribution and 3D trajectory extraction in various traffic scenes, which outperforms other comparison algorithms.
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