  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 .
