Comparative Analysis for Detecting Objects Under Cast Shadows in Video Images

Cast shadows add additional difficulties on detecting objects because they locally modify image intensity and color. Shadows may appear or disappear in an image when the object, the camera, or both are free to move through a scene. This work evaluates the performance of an object detection method based on boosted HOG paired with three different image representations in outdoor video sequences. We follow and extend on the taxonomy from van de Sande with considerations on the constraints assumed by each descriptor on the spatial variation of the illumination. We show that the intrinsic image representation consistently gives the best results. This proves the usefulness of this representation for object detection in varying illumination conditions, and supports the idea that in practice local assumptions in the descriptors can be violated.