Anomaly Detection in Traffic Surveillance Videos with GAN-based Future Frame Prediction

Abstract

It is essential to develop efficient methods to detect abnormal events, such as car-crashes or stalled vehicles, from surveillance cameras to provide in-time help. This motivates us to propose a novel method to detect traffic accidents in traffic videos. To tackle the problem where anomalies only occupy a small amount of data, we propose a semi-supervised method using Generative Adversarial Network trained on regular sequences to predict future frames. Our key idea is to model the ordinary world with a generative model, then compare a predicted frame with the real next frame to determine if an abnormal event occurs. We also propose a new idea of encoding motion descriptors and scaled intensity loss function to optimize GAN for fast-moving objects. Experiments on the Traffic Anomaly Detection dataset of AI City Challenge 2019 show that our method achieves the top 3 results with F1 score 0.9412 and RMSE 4.8088, and S3 score 0.9261. Our method can be applied to different related applications of anomaly and outlier detection in videos.