Vehicle trajectory extraction and matching using multi-UAV cooperative vehicle video data
Year: 2025
Type: article
Abstract: Unmanned Aerial Vehicles (UAVs), known for their high-altitude operation, wide coverage, and flexibility, are increasingly used as sensors for traffic data collection. However, the limited field of view of a single UAV poses challenges in capturing comprehensive traffic information required for medium-level and macro-level traffic flow analysis. To address this limitation, this study proposes a post-processing method for multi-UAV collaborative video to extract wide-area vehicle trajectories. The framework comprises four main components: vehicle detection, tracking, coordinate conversion, and trajectory matching. Specifically, YOLOv8 is utilized for vehicle detection, while the DeepSORT algorithm is employed for vehicle tracking. To transform pixel coordinates into real-world coordinates, a conversion matrix is obtained through camera calibration. For trajectory matching, a cost matrix between adjacent videos is constructed for multi-segment trajectories, and assignments are optimized using the Hungarian algorithm. The proposed framework is evaluated using four metrics: a correct trajectory matching rate of 0.964, a Root Mean Square Error (RMSE) of 0 . 46 m, a Mean Absolute Error (MAE) of 0 . 51 m, and a similarity score of 0.999. Experimental results demonstrate that the proposed framework effectively overcomes challenges in trajectory extraction and matching, making it a reliable approach for wide-area vehicle trajectory analysis.
Source: Alexandria Engineering Journal
Authors: Wang Luo, Jinjun Tang, Zhangcun Yan, Guowen Dai, Chen Yuan, Yunyi Liang
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,900
Awards: G1780401526, G4683096253, G8550290571
OpenAlex ID: W7083581367