Intelligent Human Detection and Recognition Using Advanced Deep Learning Frameworks

Dr.K.Saraswathi1  ,Mr. M. Shanmugapriyan2

1Associate Professor, Department of General Engineering, Annai Mathammal Sheela Engineering College, Erumapatty, Namakkal (DT), Tamil Nadu, India.

1saraswathimuruganams@gmail.com

2Undergraduate Scholar, PG & Research Department of Computer Science,

Nehru Memorial College (Autonomous), Puthanampatti, Tiruchirappalli (DT),

Tamil Nadu, India

2shanmugapriyan1806@gmail.com

Abstract

Human detection and recognition have become essential components of modern intelligent systems.  Advanced computer vision techniques powered by deep learning provide highly accurate identification, tracking, and classification of people in images and videos.  This paper explores the integration of image preprocessing, object detection, feature extraction, and recognition models using contemporary artificial intelligence frameworks.  Techniques such as Convolutional Neural Networks (CNN), YOLO, Faster R-CNN, and transformer-based architectures are examined for their effectiveness in real-world environments.  The study highlights the importance of data quality, preprocessing, and model optimization in improving recognition performance. Results indicate that deep learning methods significantly outperform conventional approaches in terms of accuracy, robustness, and scalability.

Keywords

Human Detection, Deep Learning, Computer Vision, YOLO, Faster R-CNN, CNN, Artificial Intelligence, Object Recognition, Image Processing.