Authors: Swami Bhagwat, Shreyash Vidhate, Darshan Shinde, Professor G. K. Gaikwad
Abstract: Human Activity Recognition (HAR) focuses on automatically identifying human actions from video streams or sensor data using computer vision and machine learning techniques. With the rapid growth of intelligent healthcare, surveillance, and smart automation systems, HAR has become an important research area. This paper presents a redesigned and implementation-oriented study of a HAR system built using OpenCV and modern learning models. The work explains the complete pipeline including video acquisition, preprocessing, feature extraction, and activity classification. Instead of relying only on theoretical descriptions, the paper emphasizes a practical modular architecture and real-time considerations. The role of deep learning models combined with OpenCV preprocessing is discussed along with system challenges such as lighting varia- tion, occlusion, and computational cost. The proposed approach highlights how lightweight processing and hybrid models can support accurate and efficient recognition suitable for real-world deployment.
