Anomaly Detection in Video Using Computationally Efficient Machine Learning Algorithms
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Overview
Anomaly detection is crucial for surveillance applications. Although humans can easily interpret normal interactions in a video and determine if an activity is unusual or not, this is a challenging task for machines. The goal of this proposal is to develop computationally efficient machine learning algorithms for detecting unusual events in surveillance videos. In particular, we design, develop, and use computationally efficient generative adversarial networks (GAN). This is a collaborative work with Prof. Cetin and Prof. Koyuncu from the ECE department of UIC, and supported by two Seagate grants.
