In this project, the researchers explored a vision-based method for monitoring crowded urban scenes involving vehicles, individual pedestrians, and crowds. Based on optical flow, the proposed method detects, tracks, and monitors moving objects. Many problems confront researchers who attempt to track moving objects, especially in an outdoor environment: background detection, visual noise from weather, objects that move in different directions, and conditions that change from day to evening. Several systems of visual detection have been proposed previously. This system captures speed and direction as well as position, velocity, acceleration or deceleration, bounding box, and shape features. It measures movement of pixels within a scene and uses mathematical calculations to identify groups of points with similar movement characteristics. The system is not limited by assumptions about the shape or size of objects, but rather identifies objects based on similarity of pixel motion. Algorithms are used to determine direction of crowd movement, crowd density, and most-used areas. The speed of the software in calculating these variables depends on the quality of detection set in the first stage. Illustrations include video stills with measurement areas marked on day, evening, and indoor video sequences. The researchers foresee that this system could be used for intersection control, collection of traffic data, and crowd control.