Transfer learning model for anomalous event recognition in big video data.

ABSTRACT: Video surveillance faces challenges due to the need for improved anomalous event recognition techniques for human activity recognition. Growing security concerns make standard CCTV systems insufficient because of high monitoring costs and operator exhaustion. Therefore, automated security systems with real-time event recognition are essential. This research introduces a semantic key frame extraction algorithm based on action recognition to minimize frame volume big video data. This approach has not been previously applied with ResNet50, VGG19, EfficientNetB7, and ViT_b16 models for recognizing anomalous events in surveillance videos. The findings demonstrate the effectiveness of this method in achieving high accuracy rates. The proposed method addresses the challenges posed by large volume

SUBMITTER: Taha RA 

PROVIDER: S-EPMC11560924 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature