Good Practices and A Strong Baseline for Traffic Anomaly Detection

Abstract

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 handcrafted 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 the postprocessing 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.

Transit Safety System Evaluation and Hotspot Identification Empowered by Edge Computing Transit Event Logging System

“…Traffic anomaly detection has been advancing both through design of new learning methods and design of new object tracking methods. In the 2021 AI City Challenge, all top-ranking methods made contributions to tracking methods (10)(11)(12).…”