Abstract
Anomaly detection in video is a challenging computer vision problem, as the classification of an event as normal or abnormal always depends on context. For instance, driving a truck on the street is considered normal, but, if the truck enters a pedestrian area, the event becomes abnormal. Considering the commonly adopted definition of abnormal events and the reliance on context, it is difficult to obtain a sufficiently representative set of anomalies for all possible contexts, making traditional supervised methods less applicable to abnormal event detection. In this talk, we will present a series of recent anomaly detection methods that are trained without direct supervision. The presented methods propose alternative object-centric approaches such as designing proxy self-supervised tasks or using pseudo-abnormal or virtual examples in an adversarial fashion. By focusing strictly on objects, object-centric approaches can significantly reduce false detection rates, forming a viable solution for real world scenarios.
For more info, please follow this link.