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Anomalous Motion Detection of Vehicles on Highway using Deep Learning
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Research in visual anomaly detection draws much interest due to applications in surveillance. Common data sets for evaluation are constructed using a stationary camera overlooking an area of interest. Despite the challenges of learning from a single class of data in an unsupervised learning paradigm, previous research shows promising results in detecting spatial as well as temporal anomalies in crowded environments. The advent of self-driving cars provides an opportunity to apply visual anomaly detection in a more dynamic application yet no data set exists to evaluate anomaly detection models in this setting. This thesis presents a novel anomaly detection data set for the problem of detecting anomalous traffic patterns from dash cam videos of vehicles on highways. I evaluate state-of-the-art unsupervised deep learning anomaly detection models as well as propose novel variations and discuss the exacerbated challenges of this new data set.