Multi-branch Neural Networks for Video Anomaly Detection in Adverse Lighting and Weather Conditions

Automated anomaly detection in surveillance videos has attracted much interest as it provides a scalable alternative to manual monitoring. Most existing approaches achieve good performance on clean benchmark datasets recorded in well-controlled environments. However, detecting anomalies is much more challenging in the real world. Adverse weather conditions like rain or changing brightness levels cause a significant shift in the input data distribution, which in turn can lead to the detector model incorrectly reporting high anomaly scores. Additionally, surveillance cameras are usually deployed in evolving environments such as a city street of which the appearance changes over time because of seasonal changes or roadworks. The anomaly detection model will need to be updated periodically to deal with these issues. In this paper, we introduce a multi-branch model that is equipped with a trainable preprocessing step and multiple identical branches for detecting anomalies during day and night as well as in sunny and rainy conditions. We experimentally validate our approach on a distorted version of the Avenue dataset and provide qualitative results on real-world surveillance camera data. Experimental results show that our method outperforms the existing methods in terms of detection accuracy while being faster and more robust on scenes with varying visibility.