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[Defense] Anomaly Detection from Videos using Graph Convolution Networks

Friday, May 7, 2021

12:00 pm - 1:00 pm

In Partial Fulfillment of the Requirements for the Master of Science
Shoumik Sharar Chowdhury
will defend his thesis
Anomaly Detection from Videos using Graph Convolution Networks


Abstract

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.


Friday, May 7, 2021
12:00PM - 1:00PM CT
Online via MS Teams

Dr. Shishir Shah, thesis advisor

Faculty, students and the general public are invited.