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
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.
Topics & Concepts
AutoencoderAnomaly detectionComputer scienceArtificial intelligenceConvolutional neural networkAnomaly (physics)Feature (linguistics)Pattern recognition (psychology)Deep learningGenerative adversarial networkGenerative grammarExtractorEngineeringPhysicsProcess engineeringCondensed matter physicsLinguisticsPhilosophyAnomaly Detection Techniques and ApplicationsNetwork Security and Intrusion DetectionArtificial Immune Systems Applications