Tuna Swarm Algorithm With Deep Learning Enabled Violence Detection in Smart Video Surveillance Systems

Abstract

In smart video surveillance systems, violence detection becomes challenging to ensure public safety and security. With the proliferation of surveillance cameras in public areas, there is an increasing need for automated algorithms that can accurately and efficiently detect violent behavior in real time. This article presents a Tuna Swarm Optimization with Deep Learning Enabled Violence Detection (TSODL-VD) technique to classify violent actions in surveillance videos. The TSODL-VD technique enables the recognition of violence and can be a measure to avoid chaotic situations. In the presented TSODL-VD technique, the residual-DenseNet model is applied for feature vector generation from the input video frames and then passed into the stacked autoencoder (SAE) classifier. The SAE model is enforced to recognize the events into violence and non-violence events. To improve the violence detection effectiveness of the TSODL-VD procedure, the TSO protocol is utilized as a hyperparameter optimizer for the residual-DenseNet model. The performance validation of the TSODL-VD procedure has experimented on a benchmark violence dataset. The experimental results demonstrate that the TSODL-VD technique accomplishes precise and rapid detection outcomes over the recent state-of-the-art approaches.