Cyclists face a disproportionate risk of injury, yet conventional crash records are too sparse to identify risk factors at fine spatial and temporal scales. Recently, naturalistic studies have used video data to capture the complex behavioural and infrastructural risk factors. A promising format is panoramic video, which can record 360$^\circ$ views around a rider. However, its use is limited by distortions, large numbers of small objects, and boundary continuity, which cannot be handled using existing computer vision models. This research proposes a novel three-step framework: (1) enhancing object detection accuracy on panoramic imagery by segmenting and projecting the original 360$^\circ$ images into sub-images; (2) modifying multi-object tracking models to incorporate boundary continuity and object category information; and (3) validating through a real-world application of vehicle overtaking detection. The methodology is evaluated using panoramic videos recorded by cyclists on London's roadways under diverse conditions. Experimental results demonstrate improvements over baselines, achieving higher average precision across varying image resolutions. Moreover, the enhanced tracking approach yields a 10.0% decrease in identification switches and a 2.7% improvement in identification precision. The overtaking detection task achieves a high F-score of 0.82, illustrating the practical effectiveness of the proposed method in real-world cycling safety scenarios.

翻译：骑行者面临较高的受伤风险，但传统事故记录过于稀疏，难以在精细时空尺度上识别风险因素。近期，自然主义研究开始利用视频数据捕捉复杂的行为与基础设施风险因素。全景视频作为一种极具潜力的记录形式，能够捕获骑行者周围360$^\circ$的视野。然而，其应用受限于图像畸变、大量小目标以及边界连续性问题，现有计算机视觉模型难以有效处理。本研究提出一种新颖的三步框架：(1) 通过分割并将原始360$^\circ$图像投影为子图像，提升全景图像上的目标检测精度；(2) 改进多目标追踪模型，以融入边界连续性及目标类别信息；(3) 通过车辆超车检测的实际应用进行验证。该方法利用骑行者于伦敦道路不同条件下记录的全景视频进行评估。实验结果表明，相较于基线方法，本方法在不同图像分辨率下均实现了更高的平均精度。此外，改进后的追踪方法使身份切换次数减少10.0%，身份识别精度提升2.7%。超车检测任务取得了0.82的高F值分数，证明了所提方法在真实世界骑行安全场景中的实际有效性。