ANOMALY DETECTION IN SURVILLENCE VIDEOS

Abstract

Anomaly detection with weakly supervised video-level labels is typically formulated as a multiple instance learning (MIL) problem, in which we aim to identify snippets containing abnormal events, with each video represented as a bag of video snippets. Although current methods show effective detection performance, their recognition of the positive instances, i.e., rare abnormal snippets in the abnormal videos, is largely biased by the dominant negative instances, especially when the abnormal events are subtle anomalies that exhibit only small differences compared with normal events. This issue is exacerbated in many methods that ignore important video temporal dependencies. To address this issue, we use add information from the optical flow which captures the temporal relation between successive frames in a video. In this project, we explored the field of video anomaly detection and reviewed existing literature on the subject, as well as related topics such as action recognition and optical flow extraction.