论文标题
在不受约束的道路上检测，跟踪和计算摩托车骑手违规行为
Detecting, Tracking and Counting Motorcycle Rider Traffic Violations on Unconstrained Roads
论文作者
论文摘要
在许多没有受限的道路交通状况的亚洲国家中，驾驶违规行为（例如不戴头盔和三骑行）是涉及摩托车的死亡人数的重要来源。确定和惩罚此类骑手对于遏制道路事故并改善公民的安全至关重要。通过这种动机，我们提出了一种方法，用于在从车辆安装的仪表板摄像头拍摄的视频中检测，跟踪和计算摩托车违规行为。我们采用基于课程的对象检测器来更好地解决诸如遮挡之类的具有挑战性的情况。我们介绍了一种新型的梯形对象边界表示，以增加鲁棒性并应对骑手 - 机循环关联。我们还引入了一个Amodal回归剂，该回归器为被遮挡的车手生成边界框。大规模不受约束的驾驶数据集的实验结果表明，与现有方法和其他消融变体相比，我们方法的优越性。
In many Asian countries with unconstrained road traffic conditions, driving violations such as not wearing helmets and triple-riding are a significant source of fatalities involving motorcycles. Identifying and penalizing such riders is vital in curbing road accidents and improving citizens' safety. With this motivation, we propose an approach for detecting, tracking, and counting motorcycle riding violations in videos taken from a vehicle-mounted dashboard camera. We employ a curriculum learning-based object detector to better tackle challenging scenarios such as occlusions. We introduce a novel trapezium-shaped object boundary representation to increase robustness and tackle the rider-motorcycle association. We also introduce an amodal regressor that generates bounding boxes for the occluded riders. Experimental results on a large-scale unconstrained driving dataset demonstrate the superiority of our approach compared to existing approaches and other ablative variants.
