REAL TIME HUMAN DETECTION AND COUNTING

Rahul S; Gokul Shreeman M; Gowtham R; Moni Ajit S

REAL TIME HUMAN DETECTION AND COUNTING

January 2024

Vol-10, Issue-1

Paper ID: 22381

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Information Technology

Keywords

Individual monitoring Human recognition Visual observation Challenges Limitations YOLO algorithm Image analysis Video frames Dataset analysis Mobile application Efficiency Real-time identification Complex backgrounds.

Abstract

Dependable individual monitoring and human recognition within visual observation settings remain significant challenges in contemporary contexts. While recent strides have been made in this field, existing solutions often come with inherent limitations, requiring individuals to be in motion, necessitating a simple background, and demanding high image resolution. This study aims to pioneer an effective methodology capable of accurately assessing the number of individuals and identifying each person within images featuring complex and cluttered scenes. The primary objective of this research is to devise an innovative approach to tackle the persistent challenges outlined above, employing the You Only Look Once (YOLO) algorithm as the cornerstone for identifying individual people within video frames. Leveraging a comprehensive dataset, this study aims to delve deeper into the intricate task of identifying and counting individual people, with the ultimate output visualized through a user-friendly mobile application interface. By harnessing the capabilities of the YOLO algorithm, the intention is to revolutionize individual monitoring by achieving accurate and real-time identification of people within video frames, even amidst complex and challenging environmental backgrounds. In essence, this research aims to pioneer a novel solution that not only addresses the existing limitations but also sets a benchmark for effective individual monitoring and human recognition, thereby significantly enhancing operational efficiencies and user experiences across diverse settings and applications

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Author Information

#	Name	Institute / Affiliation
1	Rahul S	SRM Institute of Science and Technology
2	Gokul Shreeman M	SRM Institute of Science and Technology
3	Gowtham R	SRM Institute of Science and Technology
4	Moni Ajit S	SRM Institute of Science and Technology

How to Cite

Use the following formats to cite this article in your research.

APA Style

S, Rahul, M, Gokul Shreeman, R, Gowtham, & S, Moni Ajit (2024). REAL TIME HUMAN DETECTION AND COUNTING. International Journal of Advance Research and Innovative Ideas In Education, 10(1), 338-342.

MLA Style

S, Rahul, et al. "REAL TIME HUMAN DETECTION AND COUNTING." International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 1, 2024, pp. 338-342.

IEEE Style

Rahul S, Gokul Shreeman M, Gowtham R, and Moni Ajit S, "REAL TIME HUMAN DETECTION AND COUNTING," International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 1, pp. 338-342, 2024.

Vancouver Style

S Rahul, M Gokul Shreeman, R Gowtham, S Moni Ajit. REAL TIME HUMAN DETECTION AND COUNTING. International Journal of Advance Research and Innovative Ideas In Education. 2024;10(1):338-342.

Harvard Style

S, Rahul, M, Gokul Shreeman, R, Gowtham, & S, Moni Ajit (2024) 'REAL TIME HUMAN DETECTION AND COUNTING', International Journal of Advance Research and Innovative Ideas In Education, 10(1), pp. 338-342.

Chicago Style

S, Rahul, et al. "REAL TIME HUMAN DETECTION AND COUNTING." International Journal of Advance Research and Innovative Ideas In Education 10, no. 1 (2024): 338-342.

Turabian Style

S, Rahul, et al. "REAL TIME HUMAN DETECTION AND COUNTING." International Journal of Advance Research and Innovative Ideas In Education 10, no. 1 (2024): 338-342.