An Efficient Approach for Anomaly Detection in Traffic Videos

TL;DR

This paper presents an efficient, edge-compatible traffic video anomaly detection system that combines scene change detection, background modeling, and a novel anomaly detection algorithm, achieving high accuracy with low resource requirements.

Contribution

It introduces a lightweight, multi-stage anomaly detection framework suitable for real-time edge deployment, outperforming existing methods on benchmark datasets.

Findings

Achieved an F1-score of 0.9157 on the AI City Challenge dataset.

Demonstrated the system's ability to run efficiently on edge devices.

Ranked fourth in the 2021 AI City Challenge.

Abstract

Due to its relevance in intelligent transportation systems, anomaly detection in traffic videos has recently received much interest. It remains a difficult problem due to a variety of factors influencing the video quality of a real-time traffic feed, such as temperature, perspective, lighting conditions, and so on. Even though state-of-the-art methods perform well on the available benchmark datasets, they need a large amount of external training data as well as substantial computational resources. In this paper, we propose an efficient approach for a video anomaly detection system which is capable of running at the edge devices, e.g., on a roadside camera. The proposed approach comprises a pre-processing module that detects changes in the scene and removes the corrupted frames, a two-stage background modelling module and a two-stage object detector. Finally, a backtracking anomaly detection algorithm computes a similarity statistic and decides on the onset time of the anomaly. We also propose a sequential change detection algorithm that can quickly adapt to a new scene and detect changes in the similarity statistic. Experimental results on the Track 4 test set of the 2021 AI City Challenge show the efficacy of the proposed framework as we achieve an F1-score of 0.9157 along with 8.4027 root mean square error (RMSE) and are ranked fourth in the competition.