Real-Time Human Detection and Tracking Based on Deep Learning Technique

Volume 20 No 6 (2022)

Gona M Rozhbayani , Amel Tuama ,Fadwa Al-Azzo

DOI: 10.14704/nq.2022.20.6.NQ22202

Abstract

In the field of computer vision, the detection of an object such as a human is critical for image understanding. Human tracking detection in real-time helps in providing critical information for a vast variety of intelligent system applications. This paper presents a new model for real-time human tracking detection (RTHTD) for surveillance video by using a deep learning technique based on the modified YOLOv5 model, the backbone of the modified model formed from Cross Stage Partial(CSP ), Bottleneck, and SPPF

Keywords

Human tracking detection, YOLOv5, deep learning, real-time, surveillancevideo

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