E2E-VSDL: End-to-end video surveillance-based deep learning model to detect and prevent criminal activities

Abstract

Crime detection and their prediction is a fundamental process to reduce criminal activities before they actually happen. Moreover, the detection method is vital since can it potentially can save the victim's life, avoid all-time strain, and harm to the public/private property. In addition, it can be useful in predicting the possible terrorist activities. Crime detection using deep learning models is an attention-grabbing research area. Detecting and reducing the criminal activities is imperative to develop a peaceful society. Video surveillance automates the hazardous situations and enables a law enforcement system to take effective steps towards public safety. In this paper, an end-to-end deep learning model is proposed which is based on Bi-directional gated recurrent unit (BiGRU) and Convolutional neural network (CNN) to detect and prevent criminal activities. The CNN extracts the spatial features from video frames whereas temporal and local motion features are extracted by the BiGRU from multiple frames CNN extracted features. The focused bag is created to select those video frames which indicate certain actions. Moreover, ranked-based loss is used to effectively detect and classify the suspicious activities. For classification of activities, various machine learning classifiers are used. The proposed deep learning video surveillance technique is able to track human trails and detect criminal events. The CAVIAR dataset is used to examine the proposed technique for video surveillance-based crime detection with a performance accuracy of almost 98.86%. The alerts received from the proposed technique can also be examined, demonstrates that the practiced video surveillance cameras systems can effectively detect unusual and criminal activities. In addition, the proposed technique showed considerable performance accuracy and outscored the related state-of-the-art (SOTA)DL models including CNN-LSTM, CNN, HMM, and DBN and achieved 21.88% absolute improvement in crime detection accuracy.