Hybrid model of clustering and kernel autoassociator for reliable vehicle type classification
Machine Vision and Applications
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This paper describes a vision system that recognizes moving targets such as vehicles and pedestrians on public streets. This system can: (1) classify targets {vehicle, pedestrian, others} and, for "vehicles," discriminate vehicle types and (2) estimate the main colors of targets. According to the input images to the system, the categories of targets are set as {mule (golf cart for workers), sedan, van, truck, pedestrian (single or plural), and other (such as noise)}. Their colors are set as six color groups {red, orange, yellow; green; blue, light blue; white, silver, gray; dark blue, dark gray, black; dark red, dark orange}. In this experiment, we collected images of targets from 9: 00 a.m. to 5: 00 p.m. on sunny and cloudy days as system training samples. The recognition ratio was 91.1% under the condition that both the recognition results of type and color agreed with the operator's judgment. In addition, the system can detect predefined specific targets such as delivery vans, post office vans, and police cars by combining recognition results for type and color. The recognition ratio for specific targets was 92.9%. For the classification and estimation of targets, we employed a statistical linear discrimination method (linear discriminant analysis, LDA) and a nonlinear decision rule (weighted K-nearest neighbor rule, K-NN).