Automatic detection of traffic accidents is an important emerging topic in
traffic monitoring systems. Nowadays many urban intersections are equipped with
surveillance cameras connected to traffic management systems. Therefore,
computer vision techniques can be viable tools for automatic accident
detection. This paper presents a new efficient framework for accident detection
at intersections for traffic surveillance applications. The proposed framework
consists of three hierarchical steps, including efficient and accurate object
detection based on the state-of-the-art YOLOv4 method, object tracking based on
Kalman filter coupled with the Hungarian algorithm for association, and
accident detection by trajectory conflict analysis. A new cost function is
applied for object association to accommodate for occlusion, overlapping
objects, and shape changes in the object tracking step. The object trajectories
are analyzed in terms of velocity, angle, and distance in order to detect
different types of trajectory conflicts including vehicle-to-vehicle,
vehicle-to-pedestrian, and vehicle-to-bicycle. Experimental results using real
traffic video data show the feasibility of the proposed method in real-time
applications of traffic surveillance. In particular, trajectory conflicts,
including near-accidents and accidents occurring at urban intersections are
detected with a low false alarm rate and a high detection rate. The robustness
of the proposed framework is evaluated using video sequences collected from
YouTube with diverse illumination conditions. The dataset is publicly available
at: this http URL.

本论文提出了一种在交通监控应用中实现交通事故检测的高效框架，包括基于 YOLOv4 方法的准确物体检测、基于 Kalman 滤波器以及匈牙利算法的对象跟踪，以及轨迹冲突分析的事故检测，并且用于真实交通视频数据的实验结果证明了该方法的实时应用的可行性。