Multiple Object Detection and Tracking in Panoramic Videos for Cycling Safety Analysis
<|reference_start|>Multiple Object Detection and Tracking in Panoramic Videos for Cycling Safety Analysis: Panoramic cycling videos can record 360{\deg} views around the cyclists. Thus, it is essential to conduct automatic road user analysis on them using computer vision models to provide data for studies on cycling safety. However, the features of panoramic data such as severe distortions, large number of small objects and boundary continuity have brought great challenges to the existing CV models, including poor performance and evaluation methods that are no longer applicable. In addition, due to the lack of data with annotations, it is not easy to re-train the models.   In response to these problems, the project proposed and implemented a three-step methodology: (1) improve the prediction performance of the pre-trained object detection models on panoramic data by projecting the original image into 4 perspective sub-images; (2) introduce supports for boundary continuity and category information into DeepSORT, a commonly used multiple object tracking model, and set an improved detection model as its detector; (3) using the tracking results, develop an application for detecting the overtaking behaviour of the surrounding vehicles.   Evaluated on the panoramic cycling dataset built by the project, the proposed methodology improves the average precision of YOLO v5m6 and Faster RCNN-FPN under any input resolution setting. In addition, it raises MOTA and IDF1 of DeepSORT by 7.6\% and 9.7\% respectively. When detecting the overtakes in the test videos, it achieves the F-score of 0.88.   The code is available on GitHub at github.com/cuppp1998/360_object_tracking to ensure the reproducibility and further improvements of results.<|reference_end|>
@article{guo2024multiple,
    title={Multiple Object Detection and Tracking in Panoramic Videos for Cycling
  Safety Analysis},
    author={Jingwei Guo, Meihui Wang, Ilya Ilyankou, Natchapon Jongwiriyanurak,
  Xiaowei Gao, Nicola Christie, James Haworth},
    journal={arXiv preprint arXiv:2407.15199},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15199},
    primaryClass={cs.CV cs.CY}
}