DeepGuard: real-time threat recognition using Golden Jackal optimization with deep learning model

Violence recognition in surveillance videos is a vital feature of current security systems. It can improve complete security measures, making it essential in generating safe public places and defending against unforeseen security tasks. Real risk and violence detection in surveillance videos signify a cutting-edge use of deep learning (DL) technologies. By using innovative neural networks, this plan proposes to improve safety by quickly classifying possible threats and occurrences of violence within surveillance footage. The system influences DL models to examine complex visual patterns, differentiating between normal actions and potential safety risks. This real-time detection ability allows instant responses and involvements, donating to a proactive technique in safeguarding public safety and the security of critical infrastructures. Integrating DL methods in surveillance video study showcases the probability for sophisticated, intelligent methods to enlarge classical safety measures, delivering fast and effective threat recognition in different environments. This study presents a DeepGuard model for Real-Time Threat Recognition using Golden Jackal Optimization with Deep Learning. The purpose of the DeepGuard model is to identify violence and non-violence events in the surveillance videos. In the DeepGuard model, an improved ShuffleNetv2 model can be applied to derivate the intrinsic and complex features from the input images. Besides, the Golden Jackal Optimization (GJO) model can be applied for the optimal hyperparameter tuning of the improved ShuffleNetv2 model. The DeepGuard model uses long short-term memory neural networks (LSTM-NNs) for violence detection. The performance evaluation of the DeepGuard model takes place using benchmark datasets. The experimental outcomes of the DeepGuard model obtained optimum performance with 99.00% and 98.63% when related to other techniques in terms of distinct measures.