Criminal Activity Classification in Surveillance Videos Using Deep
Learning Models
Raed Majeed1,∗, Hiyam Hatem22
1Department Computer Information Systems, College of Computer Science and Information Technology,
University of Sumer, Dhi-Qar, Iraq
2Department Computer Science, College of Computer Science and Information Technology, University of
Sumer, Dhi-Qar, Iraq
Emails: raed.m.muttasher@gmail.com; hiamhatim2005@gmail.com.
Abstract
Detecting and identifying crimes in real time represents a very necessary aspect of public safety.
Traditional systems are human based monitoring cameras, video surveillance systems are ineffective,
time consuming and prone to mistakes. Automated solutions are much needed. Using convolutional
neural networks (CNNs) to efficiently examine surveillance video footage is the main goal. This
work presents a crime detection system based on deep learning. the study utilize UCF Crime dataset
and four deep learning models: ResNet50, EfficientNetB2, Xception, and custom (CNN) were up-
graded, trained, and tested. To guarantee best model performance, the suggested approaches required
careful dataset preparation, pre-processing, and strategic data separation. By means of fine-tuning,
each model addressed the constraints of conventional techniques and enhanced feature extraction and
classification accuracy. With extraordinary performance measures of (99.53%) accuracy, (99.07%)
precision, (98.43%) recall, and a (98.69%) F1 score, experimental findings show the superiority of
the suggested system. These findings reveal the system’s high dependability in detecting and clas-
sifying criminal events, thereby far surpassing other CNN-based approaches. The model runs at an
average inference speed of (30 ms per frame on CPU), with a lightweight model size of around (20
MB), These results demonstrate the system’s scalability, efficiency, and strong potential for intelli-
gent surveillance applications. This study shows how scalable and effective deep learning models
transform crime detection in surveillance systems to support public safety.
Received: February 25, 2025 Revised: May 31, 2025 Accepted: July 06, 2025
Keywords: Anomaly Detection; UCF-Crime Dataset; Deep learning (DL); Convolutional neural networks
(CNNs); Surveillance videos