Anomalous Motion Detection on Highway Using Deep Learning
This work addresses anomaly detection for self-driving cars and surveillance, but is incremental as it adapts existing methods to a new dataset.
The paper tackled the problem of detecting anomalous traffic patterns from dash cam videos on highways by introducing the Highway Traffic Anomaly (HTA) dataset, and found that state-of-the-art models for stationary cameras do not perform well in dynamic environments, with proposed variations showing promising results.
Research in visual anomaly detection draws much interest due to its applications in surveillance. Common datasets for evaluation are constructed using a stationary camera overlooking a region of interest. Previous research has shown promising results in detecting spatial as well as temporal anomalies in these settings. The advent of self-driving cars provides an opportunity to apply visual anomaly detection in a more dynamic application yet no dataset exists in this type of environment. This paper presents a new anomaly detection dataset - the Highway Traffic Anomaly (HTA) dataset - for the problem of detecting anomalous traffic patterns from dash cam videos of vehicles on highways. We evaluate state-of-the-art deep learning anomaly detection models and propose novel variations to these methods. Our results show that state-of-the-art models built for settings with a stationary camera do not translate well to a more dynamic environment. The proposed variations to these SoTA methods show promising results on the new HTA dataset.