Anomaly Detection Using Convolutional Neural Networks for Crime Prevention: A Deep Learning Approach

Ms. Noor Unnisa, Mohammed Ehtesham Ul Baqui, Ms. Vibhavari N, Ms. Farheen Sultana, Shaik Irfan and Mohammed Mubashir Ul Baqui
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Ms. Noor Unnisa: Department of CSE Lords Institute of Engineering & Technology Hyderabad, India.
Mohammed Ehtesham Ul Baqui: Department of CSE Lords Institute of Engineering & Technology Hyderabad, India.
Ms. Vibhavari N: Department of CSE Lords Institute of Engineering & Technology Hyderabad, India.
Ms. Farheen Sultana: Department of CSE Lords Institute of Engineering & Technology Hyderabad, India.
Shaik Irfan: Department of CSE Lords Institute of Engineering & Technology Hyderabad, India.
Mohammed Mubashir Ul Baqui: Department of CSE Lords Institute of Engineering & Technology Hyderabad, India.

International Journal of Latest Technology in Engineering, Management & Applied Science, 2025, vol. 14, issue 6, 356-362

Abstract: Anomaly detection using Convolutional Neural Networks (CNNs) has emerged as a powerful tool for identifying criminal activities, including robberies, assaults, and homicides, within surveillance environments. This research presents a deep learning-based framework that leverages CNNs for spatial feature extraction and combines them with temporal modelling to recognize irregular behaviour in public safety contexts. By analysing surveillance footage and sensor-based data, the system detects anomalies in movement patterns, crowd density, and object interactions, thereby aiding in real-time threat assessment and crime prevention. The proposed method utilizes pre-trained CNN models for high-level visual representation and integrates hybrid approaches, such as CNN-LSTM and 3D CNNs, to capture spatiotemporal dynamics of suspicious activities. Our framework is tested on benchmark datasets like UCF-Crime and UAV surveillance feeds, achieving high accuracy in detecting abnormal behaviour. Furthermore, anomaly detection is enhanced using advanced feature extraction techniques and real-time classification mechanisms tailored for smart city surveillance systems.

Date: 2025
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