  Traditional object recognition approaches apply feature extraction, part
deformation handling, occlusion handling and classification sequentially while
they are independent from each other. Ouyang and Wang proposed a model for
jointly learning of all of the mentioned processes using one deep neural
network. We utilized, and manipulated their toolbox in order to apply it in car
detection scenarios where it had not been tested. Creating a single deep
architecture from these components, improves the interaction between them and
can enhance the performance of the whole system. We believe that the approach
can be used as a general purpose object detection toolbox. We tested the
algorithm on UIUC car dataset, and achieved an outstanding result. The accuracy
of our method was 97 % while the previously reported results showed an accuracy
of up to 91 %. We strongly believe that having an experiment on a larger
dataset can show the advantage of using deep models over shallow ones.
