Deep Learning Approaches for Crime Detection in Surveillance Systems: A Comprehensive Review

Abstract

The rapidly rising crime rates have necessitated advanced and automated security surveillance systems capable of robust, real-time detection, recognition, and prevention of crime. Although traditional surveillance systems have largely been deployed to enhance security and safety, they are inefficient, error-prone, and incapable of effectively processing and generating meaningful insights from the vast quantities of video data they produce. Further, they are adversely affected by extreme weather conditions and subject to human vandalism. The advent of deep learning has significantly transformed earlier automated crime detection, recognition, and prevention by enabling robust extraction and analysis of complex spatial and temporal features from surveillance videos. This research presents a comprehensive review and synthesis of various state-of-the-art deep learning architectures employed in surveillance video-based crime detection and recognition systems, including 3D Convolutional Neural Networks (3D-CNN), Residual Networks (ResNets), Recurrent Neural Networks (RNN), Bidirectional Long- and Short-Term Memory (BiLSTM), Gated Recurrent Units (GRUs), and the integration of attention mechanisms of Soft attention, hard attention, dual attention, and Multi-Head Self-Attention (MHSA). The study critically examines the architectures’ contributions to enhancing detection accuracy, recognition, and the capability to prevent crime. The review further highlights challenges associated with existing systems, including data scarcity, privacy concerns, computational complexity, data class imbalance, and limited real-world adoptability. The paper finally outlines emerging research gaps and future directions in the development of intelligent deep learning-based surveillance crime detection systems.