Anomaly detection in UAV-captured crowd images using cumulative frame segmentation and adversarial learning

Abstract

Anomaly detection in crowds using unmanned aerial vehicle (UAV) captured images is preceded by computer-aided analysis and intelligent learning algorithms. The study is pursued using conventional image processing steps and detection methods. This article introduces a novel anomaly object-detecting method, utilizing the Cumulative Frame Segmentation (AODM-CFS) approach to identify abnormalities in UAV-captured images based on variations in pixel intensity, the data from the VisDrone dataset, and UAV Anomaly Detection. The proposed method segments the maximum intensity varying pixels by examining different pixel occurrences. The cumulative frames are segmented using the maximum repeated intensity pixels to identify objects with maximum feature diversity. The pixel repetition is verified using concatenated adversarial learning, generating repeated and dissimilar pixel maps for various identified frames. These frames are updated using the pixels discovered towards the end of the image. The training for the network map is repeated using segmented frames that rely on maximum feature diversions. Therefore, the abnormal object/ human in the image is identified using the maximum dispersion frame. The proposed method increased detection accuracy by 13.46 %, segmentation precision by 14.08 %, sensitivity by 12.7 %, and specificity by 12.88 %, resulting in an 11.92 % reduction in segmentation error compared to other existing models.