Current moving object detection systems typically detect shadows cast by the moving object as part of the moving object. Unlike previous work, we have developed an approach that does not rely on any geometrical assumptions such as camera location, and ground surface/object geometry. Two examples from two different scenes with moving vehicles where shadow of vehicles either follows or precedes them. The scene includes both asphalt with different texture, and concrete.

This represents different types of background surfaces including vertical, horizontal, textured, uniform, brick and concrete. The detection algorithm performed consistently for all the geometry and surface types present in this example.

This represents an inclined and curved grass surface. As the subject and its shadow move closer to the camera the detection improves. This is also an example of a surface that exhibits highly saturated color and secularities due to the surface type of grass and angles of incidence. This is a challenging test since we do not account for secularities that introduce noise, which affects the estimation of Cb.