Recognizing vehicle in infra-red images using IMAP parallel vision board

We describe a method to recognize vehicles, in particular to recognize which make it is and which type it is. Our system employs infra-red images so that we can use the same algorithm in day time and at night. The algorithm is based on vector quantization, originally proposed by Krumm (1997), and is implemented on the IMAP parallel image processing board. Our system makes the compressed database of local features, for the algorithm, of a target vehicle from given training images in advance, and then matches a set of local features in the input image with those in the training images for recognition. This method has the following three advantages: it can detect if part of the target vehicles is occluded; it can detect if the target vehicle is translated due to running out of lanes; and we do not need to segment a vehicle part from the input images. Through outdoor experiments, we have confirmed these advantages.