Abstract: In the domain of modern security and surveillance, the delay between threat detection and response is a critical vulnerability. This paper presents AimSense, a computer vision-based threat detection system currently under development. The system utilizes the You Only Look Once (YOLO) version 11 (YOLOv11) architecture to integrate object detection (weapon recognition) with pose estimation (human skeleton analysis), enabling accurate identification of active threats based on grasping interactions rather than mere object presence. An important component of the proposed system is the Human-in-the-Loop (HITL) interface, which ensures that all the engagement decisions are verified by a human operator prior to execution. This paper describes the prototype architecture, the sector-based localization algorithm, and the optimization techniques that enable real-time performance on standard hardware.
Keywords: Computer Vision, YOLOv11, Threat Detection, Human-in-the-loop, Pose estimation, Surveillance Systems.
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DOI:
10.17148/IJARCCE.2026.15220
[1] Amal Sankar M, Albin Varghese Mathew, Ajin Anil, Jishnu Jayakumar, Ancy Das Y R, "AimSense: A Real-Time AI-Assisted Threat Detection and Response System with Human-in-the-Loop Protocol," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2026.15220
