Abstract
Produced are varieties of information, such as tracking, speed, and recognition of cars as well as traffic density from trafiic video, while there are many tasks to be solved. As an example, it is important but not easy to separate a car and shadow attached to it since a moving area includes the car and the shadow when lit by the sun. This paper presents a novel method for car shadow detection with improvement for background update. In general, an intermediate part, called penumbra, exists between shadow and lit parts. Luminance gradually changes in the penumbra but little changes in the shadow area. This leads us to a new shadow model, i.e. a shadow part consists of a concavity on the left edge, a convexity on the right edge, and very little luminance change between the two edges after differentiation of the luminance value of the shadow part. This shadow model is enhanced with another property, i.e. width of a shadow coincides with that of a moving area in the shadow part. Based on the new shadow model, an experiment is carried out showing its superior performance in shadow detection rate, where the rate is 83%, much greater than 70∼78% by other methods proposed in the past.
