FRC-SGAN based anomaly event recognition for computer night vision in edge and cloud environment

Abstract

Anomaly event recognition and identification has a crucial part in several areas, particularly in night vision environments. Conventional techniques of event recognition are hugely based upon data extracted from certain images for classification purposes. This needs users to select suitable features to establish the feature depictions for actual images per definite situations. Manual feature selection is laborious as well as heuristic tasks and the features obtained in this manner generally have worse robustness. Here, a Faster Region-based Convolutional fused Social Generative Adversarial Network (FRC-SGAN) is designed for anomaly event recognition in a night vision environment. At the cloud, key frame extraction, pre-processing, feature extraction, human detection (HD) and anomalous event recognition are carried out. Initially, input video from the database is subjected to perform pre-processing. The visibility enhancement is utilized for pre-processing. Thereafter, features like ResNet features, texture features and statistical features are extracted. Then, HD is accomplished by DeepJoint segmentation with chord distance. Finally, anomalous detection is done by FRC-SGAN that is the incorporation of Fast Regional Convolutional Neural Network (FR-CNN) and Social Generative Adversarial Network (SGAN). In addition, FRC-SGAN acquired 90.8% of accuracy, 89.7% of precision, and 89.2% of recall.