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By: Eshwar Gopad, Rohan Bihari Jangid, Riya Sandeep Salvi, and Mahesh Sunil Belhekar.
Student, Department of Computer Engineering, RD’s Shree Chhatrapati Shivajiraje College of Engineering, Pune, Maharashtra, India
The real-time human detection and counting system leverages deep learning and computer vision to detect individuals, classify gender, and estimate age from live video feeds or CCTV footage. Implemented using flask as a lightweight web framework, the system manages user authentication, detection control, and real-time result display. A Python-based detection script operates asynchronously, ensuring smooth execution, while results – including counts of males, females, and total individuals – are stored in a JSON file for persistence and retrieval. Our project addresses the growing demand for accurate crowd monitoring solutions, particularly in high-traffic environments like shopping malls and public spaces. Using the YOLO V8 model for human detection and Deep SORT for object tracking, the system maintains precise tracking and counting of individuals. TensorFlow optimizes performance on GPU, enabling real-time processing with enhanced speed and accuracy. To ensure adaptability, the system tackles environmental challenges such as varying lighting conditions, camera angles, and low-resolution imagery. Advanced techniques, like frame differencing and histogram of oriented gradients, improve detection accuracy, while an expectation–maximization model enhances people’s localization in minimal-movement scenarios. Beyond security and surveillance, this project has significant applications in retail analytics and crowd management, aiding businesses in optimizing space utilization and improving operational efficiency. Extensive testing demonstrates the system’s high accuracy under diverse conditions, establishing it as a reliable tool for modern automation and real-time monitoring needs. By integrating state-of-the-art deep learning techniques, our system presents a scalable and robust solution for intelligent human detection and analytics in dynamic environments.
People counting, human detection, real-time tracking, deep learning, YOLOv3, computer vision, TensorFlow, crowd management
Citation:
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