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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


Hundreds of thousands of hours of video are recorded by surveillance cameras every day. Although much object detection and person detection and even anomaly detection is carried out on these video feeds~\cite{Kim2011}, the methods used have still been fairly traditional and repetitive: from bottom-up approaches using low-level features or leveraging the advent of convolutional neural networks or, more recently, image transformers. We propose 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 are treated 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.