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@article{195510,
        author = {Ajinkya Naik and Chaitanya Utekar and Prathmesh Bhatpure and Sahil Lukade},
        title = {Crowd Detection and Management Using Deep Learning and Computer Vision},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {15169-15173},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195510},
        abstract = {Crowd detection and management have become a critical challenge in modern urban environments, public events, and transportation hubs. Uncontrolled crowd gatherings can lead to dangerous situations including stampedes, accidents, and public safety threats. This paper presents a real-time crowd detection and management system that leverages deep learning techniques, specifically Convolutional Neural Networks (CNN) and the You Only Look Once (YOLOv8) object detection framework, combined with computer vision algorithms for accurate crowd density estimation and anomaly detection. The proposed system processes live video feeds from surveillance cameras, detects individual persons, estimates crowd density, and triggers automated alerts when crowd thresholds are exceeded. Experimental results demonstrate that the system achieves a detection accuracy of 94.7% with a processing speed of 28 frames per second on standard hardware, making it suitable for real-time deployment. The system also incorporates a crowd flow analysis module that predicts potential bottlenecks and assists authorities in proactive crowd management decisions.},
        keywords = {Crowd Detection, Crowd Management, Deep Learning, YOLOv8, Convolutional Neural Network, Computer Vision, Density Estimation, Anomaly Detection, Surveillance, Object Detection.},
        month = {April},
        }