SecureVision: A Lightweight Real-Time Framework for Aggression and Unattended Object Detection in Surveillance Videos
K.S. Sunil, Devadarsh Babu, Nayana Raveendran, Sangeeth Sagaran K S, A Shivadath · 2025
The increasing prevalence of unattended baggage and aggressive behavior in public spaces poses significant risks to public safety, ranging from theft to potential terrorism threats. This paper presents SecureVision, a lightweight, real-time framework for detecting such anomalies in surveillance video. Leveraging YOLOv5 for object detection and BoT-SORT for multi-object tracking, along with handcrafted logic for threat assessment, SecureVision achieves high detection accuracy across classes including person, bag, and aggressive_posture. The system is deployed with a real-time web dashboard using Firebase and Kivy, and delivers inference speeds of approximately 30 FPS. Experimental results demonstrate that SecureVision is both efficient and deployable, offering a reliable solution for security monitoring in resource-constrained environments.