ANOMALY DETECTION IN SURVILLENCE VIDEOS
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I.O.E. Pulchowk Campus
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.
Description
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.