• Sidwadkar Ishwari Prasant
• Bandgar Pranjali Manohar
• Khadke Akshda Milind
• Pathan Tahesin Gassar
• Prof. S. R. Bhujbal

Targeting the current Covid 19 pandemic situation, this paper identifies the need of crowd management. It is a comprehensive real-time people counting system that utilizes the advanced YOLOv8 object detection algorithm. The proposed system aims to accurately track individuals within a video stream and provide precise counts of people entering and exiting specific areas of interest. The system combines state-of-the-art computer vision techniques, leveraging the robust object detection capabilities of YOLOv8, along with efficient tracking mechanisms and region-based analysis. The system demonstrates exceptional accuracy and robustness in people-counting tasks through extensive experimental evaluations conducted across various scenarios. The results highlight the system's effectiveness in crowd management, occupancy analysis, and surveillance applications.

People counting, Real- Time System, YOLOv8 Algorithm, Computer Vision, Object Detection.

"People Counting, Capturing Image Using YOLO Deep Learning Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.h728-h734, April-2024, Available :http://www.jetir.org/papers/JETIR2404784.pdf

"People Counting, Capturing Image Using YOLO Deep Learning Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. pph728-h734, April-2024, Available at : http://www.jetir.org/papers/JETIR2404784.pdf