The detection of traffic anomalies is a critical component of the intelligent city transportation management system. Previous works have proposed a variety of notable insights and taken a step forward in this field, however, dealing with the complex traffic environment remains a challenge. Moreover, the lack of high-quality data and the complexity of the traffic scene, motivate us to study this problem from a hand-crafted perspective. In this paper, we propose a straightforward and efficient framework that includes pre-processing, a dynamic track module, and post-processing. With video stabilization, background modeling, and vehicle detection, the pro-processing phase aims to generate candidate anomalies. The dynamic tracking module seeks and locates the start time of anomalies by utilizing vehicle motion patterns and spatiotemporal status. Finally, we use post-processing to fine-tune the temporal boundary of anomalies. Not surprisingly, our proposed framework was ranked $1^{st}$ in the NVIDIA AI CITY 2021 leaderboard for traffic anomaly detection. The code is available at: https://github.com/Endeavour10020/AICity2021-Anomaly-Detection .
翻译:发现交通异常现象是智能城市交通管理系统的一个关键组成部分。以前的工作提出了各种值得注意的见解,并在这一领域向前迈出了一步,然而,处理复杂的交通环境仍然是一项挑战。此外,缺乏高质量的数据和交通场面的复杂性,促使我们从手工制作的角度研究这一问题。在本文件中,我们提出了一个直接而有效的框架,其中包括预处理、动态轨道模块和后处理。通过视频稳定、背景建模和车辆探测,支持处理阶段旨在产生候选异常现象。动态跟踪模块利用车辆运动模式和空间状态寻找并定位异常现象的起始时间。最后,我们利用后处理来微调异常现象的时间边界。毫不奇怪,我们提议的框架在VIVDIA AI CITY 2021 年的“交通异常现象检测”中排名为1美元。代码见:http://github.com/Endevol10020/AICity2021-AnnomalySudition。