[Defense] Anomaly Detection from Videos using Graph Convolution Networks
Friday, May 7, 2021
12:00 pm - 1:00 pm
Shoumik Sharar Chowdhury
will defend his thesis
Anomaly Detection from Videos using Graph Convolution Networks
Hundreds of thousands of hours of video is recorded by surveillance cameras everyday. Although a lot of object detection and person detection and even anomaly detection is carried out on these video feeds, the methods used have still been fairly traditional and repetitive. This thesis proposes a novel semi-supervised learning method to detect anomalies from a pedestrian dataset by representing each frame in the video feed as a graph. We create a graph embedding from video frames, where objects are treated as nodes and hand-crafted features between the objects as edges. This embedding is then combined with convolutional features to detect anomalies.
12:00PM - 1:00PM CT
Online via MS Teams
Dr. Shishir Shah, thesis advisor
Faculty, students and the general public are invited.