Rogers, Scott

The authors present a vision-based method for monitoring crowded urban scenes 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. It is not limited by assumptions about the shape or size of objects, but identifies objects based on similarity of pixel motion. Algorithms are used to determine direction of crowd movement, crowd density, and mostly 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 authors foresee that this system could be used for intersection control, collection of traffic data, and crowd control.

This report describes a system for monitoring bicycle activity in sequences of gray scale images from a stationary camera. Applications for such a system include determining the use and congestion of bicycle paths. The output of the system is a count of the number of bicycles detected in the image sequence. The system uses a simple model of two circular objects separated by relatively known distance, with four levels of abstraction: raw images, blobs, edge images, and the bicycle model. The system was implemented on a dual Pentium computer equipped with a Matrox imaging board and achieved a peak performance of eight frames per second. Experimental results based on outdoor scenes show promising results for a variety of weather conditions.