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NEW ERA OF VISION TO ENVISION USING YOLO

Authors:

S. Renuka Sree, B. Harish, B. Haridharshan, R. Divakar, MS. P. Raja Rajeswari,

MR. D. Pavankumar

Abstract— We provide a new approach to crowd counting in this research that makes use of the You Only Look Once (YOLO) algorithm. We show how well YOLO performs in precisely identifying and counting people in congested environments. Our method seeks to overcome the difficulties associated with crowd observation and analysis in real time. Urban planning, public safety, and event management are just a few of the many areas where crowd counting is important and dynamic. Occlusions, size variations, and congested settings are only a few of the challenges that traditional approaches frequently face in providing precise and timely crowd estimates. This study presents a unique method for accurate and efficient crowd counting that leverages the You Only Look Once (YOLO) algorithm. Through the utilization of YOLO's sophisticated object identification capabilities, our suggested approach provides a reliable way to compute crowd sizes in real-time situations. We provide an in-depth examination of the difficulties involved in crowd counting and show, by intensive testing and assessment on many datasets, how successful our YOLO-based method is. Our findings demonstrate the People counting, Image-based crowd estimation, Video analytics, Data preprocessing, Performance evaluation.

Keywords— Crowd counting, Object detection, YOLO (You Only Look Once), Deep learning, Computer vision, Convolutional neural networks (CNNs), Image processing, Crowd analysis, Real-time monitoring, Surveillance systems,