Local-feature based vehicle recognition in infra-red images using parallel vision board

The paper describes a method for vehicle recognition, in particular, for recognizing a vehicle's make and model. Our system employs infra-red images so that we can use the same algorithm both day and night. Originally, the algorithm was the eigen-window method based on local features, but it has been changed to a vector quantization based algorithm which was originally proposed by J. Krumm (1997), to implement on an IMAP parallel image processing board. Any of these systems, based on both the eigen-window method and the vector quantization method, make a compressed database of local features for the algorithm of a target vehicle from given training images in advance; the system then matches a set of local features in the input image with those in training images for recognition. This method has the following three advantages: (1) it can detect even if part of the target vehicle is occluded; (2) it can detect even if the target vehicle is translated due to running out of lanes; (3) it does not require us to segment a vehicle from input images. The above advantages have been confirmed by performing outdoor experiments.