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Human Counting With Gender Classification Using OpenCV

  • Unique Paper ID: 175459
  • Volume: 11
  • Issue: 11
  • PageNo: 3146-3150
  • Abstract:
  • With the increasing need for crowd management in public spaces, workplaces, and events, an effective overcrowd detection system is essential for ensuring safety and compliance with occupancy regulations. This project, Overcrowd Detection and Human Count, is a computer vision-based application designed to analyse images, detect the number of individuals present, classify them based on gender, and compare the total count against a predefined threshold to determine if overcrowding occurs. The system's interface allows users to upload an image and specify the maximum allowed limit for the number of people in the given space. Utilizing deep learning-based object detection techniques, the system accurately counts the number of individuals in the image and classifies them as male or female. If the detected count surpasses the specified threshold, the application triggers an “Overcrowd Detected” alert, accompanied by an audible warning to notify users of a potential overcrowding situation. Additionally, the system can generate an automated email notification to alert relevant authorities or administrators about the detected overcrowding event. Conversely, if the number of people is within the allowed limit, the system displays an “All is OK” message, indicating a safe occupancy level. The system can be adapted for various real-world applications, including crowd control in public events, monitoring occupancy in offices or classrooms, managing foot traffic in retail stores, and ensuring compliance with social distancing guidelines. One of the key advantages of this system is its ability to function efficiently without requiring expensive surveillance equipment. Instead, it processes static images and applies object detection algorithms to extract meaningful insights about occupancy levels. The system can be further enhanced to support real-time video feed analysis, automated reporting, and integration with access control mechanisms for dynamic crowd management. By providing a simple yet effective solution for monitoring occupancy levels, this project contributes to improving public safety, optimizing space utilization, and assisting organizations in maintaining regulatory compliance. Future improvements may include incorporating AI-driven crowd density estimation, real-time video stream processing, and enhanced alert mechanisms such as mobile push notifications to further refine its efficiency and usability.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 11
  • PageNo: 3146-3150

Human Counting With Gender Classification Using OpenCV

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