Deep-Learning-Based Multi-Camera Framework for Indoor Human Detection and Presence Management

Abstract

Currently, in high-density indoor environments such as businesses and factories, managing human presence and access control remains a significant challenge. Traditional access control systems based on facial recognition or card scanning typically only record authentication events at the point of entry. Therefore, continuous monitoring, presence detection, or restricted area surveillance are limited. This paper proposes an integrated indoor person detection and management framework based on centralized multi-camera processing for deployment-oriented identification surveillance. The proposed framework combines SCRFD and ArcFace to perform enrollment-based face recognition and distinguish between enrolled and unknown identities. During the experimental evaluation, the facial recognition module using the SCRFD 2.5G configuration achieved a recognition accuracy of approximately 88.2%. YOLOv11n is integrated with DeepSORT to detect and continuously track individuals within the monitored area. Experimental results showed the system achieving an average processing performance of 10.6 FPS, demonstrating the feasibility of the proposed architecture for small- to medium-scale indoor surveillance applications. Additionally, this system framework integrates event-driven spatial analysis using virtual boundaries and surveillance zones to support entry/exit counting, presence monitoring, and intrusion detection in restricted areas. Experimental results demonstrate that the proposed system framework provides consistent identification monitoring performance, stable multi-object tracking capabilities, and an efficient event management mechanism for typical indoor surveillance scenarios. This work offers a centralized, deployment-oriented surveillance architecture suitable for practical indoor security management and access control applications utilizing multiple cameras.