Integration of DeepSORT and RFID Technology for Enhanced Human Tracking

Abstract

Human activity tracking enhances safety and reduces the risk of people getting lost or kidnapped. This paper presents a human tracking system using the YOLOv8n detection model, DeepSORT tracking algorithm, and RFID technologies on low-power devices like the Raspberry Pi.

The passive RFID tags, which do not require batteries, make the system lightweight and maintenance-free. The Raspberry Pi Model V3 camera, with an 8-megapixel Sony IMX219 sensor, captures video at 640x480p90 resolution.

The YOLOv8n algorithm was trained on 2292 images, achieving an accuracy of 0.992 for mAP50 and 0.902 for mAP50-95. After integrating it with DeepSORT, the system achieved MOTA = 0.973684 and MOTP = 0.438766 at 30 fps.

In real time, tracking for 20 frames yielded MOTA = 1.0 and MOTP = 0.13. The UHF RFID reader detected tags at a distance of 1.5 meters.