Understanding and dealing with safety aspects of crowd dynamics in mass gatherings of people related to sports, religious and cultural activities is very important, specifically with respect to crowd risk analysis and crowd safety. Historical trends from the Kingdom of Saudi Arabia hosting millions of pilgrims each year during the Hajj and Omrah seasons suggest that stampedes in mass gatherings occur frequently and highlight the importance of studying and dealing with the crowd dynamics more scientifically. In this regard, efficient monitoring and other safe crowd management techniques have been used to minimize the risks associated with such mass gathering. An example of these techniques is real-time monitoring of crowd using a UAV (Unmanned Aerial Vehicle); this technique is becoming increasingly popular with the objective to save human lives, preserve environment, protect property, keep the peace, and uphold governmental authority. In this paper, a crowd monitoring system for pedestrians has been proposed and tested. The system has deployed crowd monitoring technique using real-time images taken by UAVs; the collected data was investigated, and crowd density was estimated using image segmentation procedures. A color-based segmentation method has been employed to detect, identify and map crowd density under different camera positions and orientations. Furthermore, the associated anomalies/outliers which may lead to non-classification of features have been eliminated using image enhancement tools. The paper presents a crowd monitoring system for pedestrians that can contribute to an area of research still in its infancy. The proposed system is a valuable tool in terms of facilitating timely decisions, based on highly accurate information. The results show that the used image segmentation technique has the capability of mapping the crowd density with an accuracy level up to 80%.