Video Display Improvement by Using Collaborative Edge Devices with YOLOv11

Abstract

Efficient human detection in video streams is essential for various IoT applications, including surveillance, smart cities, intelligent transportation systems (ITSs), and industrial automation. However, resource-constrained IoT devices often face limitations in handling deep learning-based object detection. This study proposes a collaborative edge computing framework utilizing multiple Raspberry Pi-based IoT devices to improve YOLOv11-based human detection performance. By distributing video frames across multiple edge devices, the proposed system effectively balances the computational load, resulting in an increase in the FPS (Frames Per Second) for processed video outputs. The experimental results confirm that as more edge devices collaborate, overall video processing efficiency improves, demonstrating the feasibility of distributed object detection for scalable and cost-effective IoT-based video analytics. In particular, the proposed approach holds significant potential for ITS applications such as pedestrian monitoring at intersections, real-time incident detection, and enhancing traffic safety by enabling responsive and decentralized analysis at the edge.