Recognizing vehicles in infrared images using IMAP parallel vision board

• Published in: IEEE transactions on intelligent transportation systems Vol. 2; no. 1; pp. 10 - 17
• Main Authors: Kagesawa, M., Ueno, S., Ikeuchi, K., Kashiwagi, H.
• Format: Journal Article
• Language: English
• Published: Piscataway, NJ IEEE 01.03.2001; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Artificial intelligence; Automotive components; Computer science; control theory; systems; Exact sciences and technology; Image recognition; Image segmentation; Image sensors; Infrared imaging; Mathematical models; Night; Pattern recognition. Digital image processing. Computational geometry; Recognition; Roads; Space vehicles; Switches; Target recognition; Training; Vehicle detection; Vehicles; Velocity measurement; Infrared thermography; Vehicle; Image recognition; Vector quantization; Image processing; Vision; Infrared imaging; Database; Parallel processing; Pattern recognition; Color image
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/6979.911081

Describes a method for vehicle recognition, in particular for recognizing make and model. Our system takes into account the fact that vehicles of the same make and model number come in different colors; it employs infrared (IR) images, thereby eliminating color differences. The use of IR images also enables us to use the same algorithm both day and night. This ability is particularly important because the algorithm must be able to locate many feature points, especially at night. Our algorithm is based on a configuration of local features. For the algorithm, our system first makes a compressed database of local features of a target vehicle from training images given in advance; the system 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: (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 the lanes; and (3) it does not require us to segment a vehicle part from input images. We have two implementations of the algorithm. One is referred to as the eigenwindow method, while the other is called the vector-quantization method. The former method is good at recognition, but is not very fast. The latter method is not very good at recognition but it is suitable for an IMAP parallel image-processing board; hence, it can be fast. In both implementations, the above-mentioned advantages have been confirmed by performing outdoor experiments.