F³DNN-Net: behaviours violence detection via fine-tuned fused feature based deep neural network from surveillance video

Detecting violence in images has emerged as an important area of research and application as digital imaging and computer vision advance. Detecting and assessing violent content automatically in visual data holds great promise for enhancing public safety, security, and content moderation. Violent incidents are difficult to detect because of disruptions in optical flow, complications caused by camera aperture variations, and difficulties with illumination and feature tracking. In this work, a novel F³DNN-Net has been proposed for violence detection in images. The input videos from the surveillance camera are initially converted into frame sequences and pre-processed. The Yolov8 (You Only Look Once) algorithm is employed to detect objects within pre-processed frames. Simultaneously, the ego algorithm is applied to the pre-processed images for motion estimation, ensuring comprehensive coverage of information. Afterwards, the features are extracted using the fined feature extraction phase as independent component analysis (ICA), and the selected features are selected using the Dingo algorithm. Then, the fine-tuned fused (FF) features are implemented in the deep neural network (DNN) approach for the categorization of violent and non-violent cases. According to the findings, the proposed F³DNN-Net detects violence in images with an accuracy rate of 99.33%. The proposed DNN enhances an overall F1-score of 10.46%, 6.91%, and 4.44%, better than Alex Net, ResNet, and Dense Net. The proposed F³DNN-Net enhances the overall accuracy by 6%, 0.3%, and 29.33% better than Inception-Resnet-V2, CNN, LSTM, and ResNet50, respectively.