Multi lane vehicle orientation extractions using multi views from roadside cameras

A method to extract views of different orientations of a vehicle captured using multi cameras on roadside is proposed. We expect the use of multi views would increase classification performance in tasks such as identifying vehicle types/makes. This paper does not discuss classification work in details; it accepts the concept that with more data obtained through multi camera views, the use of distinctive orientations only would improve classifier´s performance. Prior to this, we have to resolve practical issues such as identifying condition of vehicle merges and shadow. We use correlated data from multi cameras to find the most optimized cut for a merge situation. We also propose a novel approach of removing vehicle´s shadow using blob reconstruction technique. Views of different vehicle orientations (in our experiment, left, rear, right) are interpreted using a 3D graph fitting on images from multi cameras.