Behavioral Analysis of Surveillance Video for Criminal Profiling Using Motion Features
DOI:
https://doi.org/10.15662/IJEETR.2026.0802251Keywords:
Criminal Profiling, Video Surveillance, Behavioral Analysis, Motion Detection, Random Forest, Anomaly Detection, Smart City SecurityAbstract
The rapid growth of CCTV surveillance systems in urban and semi-urban areas has significantly enhanced public safety; however, their effectiveness remains limited due to reliance on manual monitoring. Human operators often miss critical events as a result of fatigue and reduced attention over prolonged periods, thereby highlighting the need for automated intelligent solutions. This paper proposes a machine learning-based framework for behavioral profiling from CCTV video using motion pattern analysis. Unlike traditional systems that focus primarily on explicit violence detection, the proposed approach captures subtle motion dynamics associated with suspicious or pre-criminal activities, such as abrupt movements and irregular motion patterns. The surveillance footage is processed using OpenCV techniques, including frame extraction and frame differencing, to quantify motion across consecutive frames. From these processed representations, statistical features such as mean motion intensity, peak magnitude, and motion variance are extracted. These features are then utilized to train a Random Forest classifier that categorizes activities into low-risk and high-risk profiles. The proposed model enhances detection reliability while maintaining computational efficiency, making it suitable for scalable deployment. Overall, this behavior-based approach improves early warning capabilities and supports proactive crime prevention in modern surveillance systems.
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