Analysis of traffic flow in urban areas using web cameras

With the development of the Internet and the omnipresence of video cameras, the amount of visual information available to individuals with access to the world wide web has increased by orders of magnitude in the last few years. Due to cost and communication constraints, this information is usually of rather low quality. The cooccurrence of a large number of sensors and the low quality of every single sensor has the potential to create a new episteme in computer vision. This paper presents an example of how certain limitations of a single sensor can be overcome by using the sensor multiplicity. Using a number of web cameras available in the Seattle area, the paper performs first a simple qualitative traffic analysis from each single camera. Since the cameras provide images at a very low rate (1 image every 2 minutes), standard techniques based on motion detection are unusable, and the paper proposes a simple approach based on image variance. Then, the data are integrated using the structure of the Seattle highway system and network tomography to determine the major flows of traffic at different times of the day.