This project explores and applies methods of image analyzation in several forms in order to monitor the condition and health of a crowd. Stampedes, congestion, and traffic all occur as a result of inefficient crowd management. The software developed identifies congested areas and determines solutions to avoid congestion based on live data. The data is processed by a local device which is fed via camera. This method was tested in simulation and proved to create a more efficient and congestion-free scenario. Future plans include depth sensing for automatic calibration and suggested course of action.