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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Balti, Carissa Fathinah
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Automotive Engineering Computer Science & IT Control & Systems Engineering

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People Counting in Sample Video Footage Using CNN Integrated with YOLOv5 Aulia, Ahmad Hasan Faqih; Balti, Carissa Fathinah; Anatasya, Keisyah Zahra; Mindara, Gema Parasti; Giri, Endang Purnama
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1933

Abstract

Accurate people counting in dynamic environments remains challenging due to variations in lighting, complex backgrounds, and occlusion. This study proposes a video-based people counting system leveraging a Convolutional Neural Network (CNN) integrated with the YOLOv5 object detection model. The system applies a structured preprocessing pipeline, including frame extraction, normalization, and noise reduction, to enhance data consistency before detection. The model was evaluated using ten real-world campus video sequences to assess detection reliability and counting accuracy. Experimental results demonstrate that the proposed method achieves high precision and recall for real-time detection across diverse scenarios. Performance degradation was observed in frames containing dense crowds or low illumination, indicating limitations under extreme conditions. These findings validate the feasibility of lightweight CNN-based detectors for surveillance and monitoring applications, while highlighting the need for larger datasets and optimized training strategies to improve robustness in more complex environments.
Co-Authors Anatasya, Keisyah Zahra Aulia, Ahmad Hasan Faqih Endang Purnama Giri Mindara, Gema Parasti