Detection and localization of anomalies in video surveillance using novel optimization based deep convolutional neural network

Nowadays, the demand for the surveillance applications is increased in order to guarantee the safety and security of the people/society. Due to the rapid growth in surveillance, human intervention is required to process the human behaviors in monitoring the anomaly attempts. Hence, this research proposes an automatic anomaly detection model for monitoring the anomalies from the surveillance videos. Accordingly, hierarchical-based social hunting optimization tuned Deep-convolutional neural network (HiS- Deep CNN) is proposed for video anomaly detection for which the object detection and tracking are the done initially. The detection improvement of the classifier is based on the training algorithm, hierarchical social hunting optimization (HiS) algorithm, which is designed based on the hybrid characteristics inherited from timber wolf and Ateles geoffrogis search agents. Moreover, the hierarchical social hunting strategy tracking of the video object is done using the features of Minimum output sum of squared error tracking algorithm (MOSSE) and Simple moving average based algorithm (SMA). The effectiveness of the hierarchical-based social hunting tuned Deep-convolutional neural network anomaly detection model is analyzed concerning the indices, such as sensitivity, accuracy, specificity, and Multiple Object Tracking Precision, which is 96.62%, 96.56%, 96.14%, and 0.994, respectively.