Irfan Haider

Papers

Total Citations

H-Index

About

Irfan Haider is a researcher focused on advancing the field of computer vision and machine learning, with a particular emphasis on Human Activity Recognition (HAR). His most-cited work, "Traditional Features based Automated System for Human Activities Recognition" (2020), introduces a novel method that fuses and selects traditional features to improve the accuracy and efficiency of HAR systems. This contribution is significant for real-world applications such as video surveillance, robotics, and intelligent monitoring, where reliable activity detection is critical. With over 5 citations, this paper has already garnered attention for its practical approach to feature engineering, bridging the gap between classical computer vision techniques and modern automation needs. Haider’s work stands out for its systematic methodology and potential to enhance automated systems in dynamic environments. His research not only addresses key challenges in HAR but also provides a foundation for future innovations in human-centered computing. As a rising voice in the field, Irfan Haider continues to contribute to the development of robust, real-time recognition systems that are both efficient and scalable.