Abstract: Accurate people counting is essential for crowd management, smart surveillance analytics, and crowd monitoring. Manually counting people is a tedious task that is vulnerable to error.
In this paper, a people counting system based on deep learning and computer vision is proposed. The proposed system is based on the use of pre-trained convolutional neural networks like YOLO for people detection. The proposed system is able to perform under various lighting conditions.
Experimental results show that the proposed system is accurate and can perform in real-time. The results show that the proposed system is suitable for smart city applications.
Keywords: People Counting, Deep Learning, Computer Vision, Object Detection, YOLO.
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DOI:
10.17148/IARJSET.2026.13467
[1] Dr.C.Karpagavalli, Dr.M.Kaliappan, A.Ganesh Aravind, "Deep Learning-Based Crowd Management with Real-Time Analytics," International Advanced Research Journal in Science, Engineering and Technology (IARJSET), DOI: 10.17148/IARJSET.2026.13467
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