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

This paper describes a method for classifying vehicle types, for example, small vehicles, sedans, and buses. For this classification, our system, based on local-feature configuration, needs many local features; thus it employs infrared images to enable us to use the same algorithm both day and night, and to eliminate concern about colors of vehicles. The algorithm is based on our previous work, which is a generalization of the eigenwindow method. This method has the following three advantages: (1) It cast detect even in cases where parts of the vehicles are occluded. (2) It can detect even if vehicles are translated due to running out of the lanes. (3) It does not require us to segment vehicle areas from input images. We developed a vehicle segmentation system using B-snake technique to obtain many training images. We then implemented our algorithm on the IMAP-vision board in order to verify the above advantages of our vehicle classification method by performing outdoor experiments.