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Smart Structures and Systems Volume 34, Number 3, September 2024 , pages 157-169 DOI: https://doi.org/10.12989/sss.2024.34.3.157 |
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Trajectory monitoring of inland waterway vessels across multiple cameras based on improved one-stage CNN and inverse projection |
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Yitian Han, Dongming Feng, Ye Xia, Rong Lin, Chan Ghee Koh and Gang Wu
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| Abstract | ||
| Accidents involving inland waterway vessels have raised concerns regarding monitoring their navigation tracks. The economical and convenient deployment of video surveillance equipment and computer vision techniques offer an effective solution for tracking vessel trajectories in narrow inland waterways. However, field applications of video surveillance systems face challenges of small object detection and the limited field of view of cameras. This paper investigates the feasibility of using multiple monocular cameras to monitor long-distance inland vessel trajectories. The one-stage CNN model, YOLOv5, is enhanced for small object detection by incorporating generalized intersection over union loss and a multi-scale fusion attention mechanism. The Bytetrack algorithm is employed to track each detected vessel, ensuring clear distinction in multiple-vessel scenarios. An inverse projection formula is derived and applied to the tracking results from monocular camera videos to estimate vessel world coordinates under potential water level changes in long-term monitoring. Experimental results demonstrate the effectiveness of the improved detection and tracking methods, with consistent trajectory matching for the same vessel across multiple cameras. Utilizing the Savitzky-Golay filter mitigates jitter in the entire final trajectory after timing-alignment merging, leading to a better fit of the dispersed trajectory points. | ||
| Key Words | ||
| attention mechanism; multiple cameras; multiple object tracking; object detection; vessel trajectory monitoring | ||
| Address | ||
| (1) Yitian Han, Dongming Feng, Rong Lin, Gang Wu: National and Local Joint Engineering Research Center for Intelligent Construction and Maintenance, Southeast University, Nanjing 211189, China; (2) Yitian Han, Chan Ghee Koh: Department of Civil and Environmental Engineering, National University of Singapore,117576, Singapore; (3) Ye Xia: of Civil Engineering, Tongji University, Shanghai 200092, China. | ||