Authors: Riya Abhyankar, Arundhati Melinkeri, Trupti Mahajan, Dr. Sinu Nambiar
Abstract: Urban populations are rapidly growing with large scale public events , hence monitoring the crowd behaviours and count has become a necessity for modern surveillance systems. An accu-rate crowd count helps to estimate number of people in each area while anomaly detection helps identifying situations such as overcrowding, abnormal patterns. The rpaper will present the YOLOv11 object detection algorithm and apply it with K-means clustering to count crowds and detect anomalies. The proposed system aims to provide a simple yet effective mechanism for real-time crowd analysis by leveraging detection, clustering, and visualization techniques. The experi-ment results demonstrate the ability of the system to measure crowd size and identify crowded zones, making it a useful tool for surveillance purposes in urban environments.
