3D-Convolutional Neural Network with Generative Adversarial Network and Autoencoder for Robust Anomaly Detection in Video Surveillance

Wonsup Shin, Seok Jun Bu, Sung Bae Cho

Research output: Contribution to journalArticle

Abstract

As the surveillance devices proliferate, various machine learning approaches for video anomaly detection have been attempted. We propose a hybrid deep learning model composed of a video feature extractor trained by generative adversarial network with deficient anomaly data and an anomaly detector boosted by transferring the extractor. Experiments with UCSD pedestrian dataset show that it achieves 94.4% recall and 86.4% precision, which is the competitive performance in video anomaly detection.

Original languageEnglish
Article number2050034
JournalInternational Journal of Neural Systems
Volume30
Issue number6
DOIs
Publication statusPublished - 2020 Jun 1

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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