A Vision System that Recognizes Objects on General Streets

Abstract

This paper describes a vision based monitoring system which (1) classifies targets (vehicles and humans) based on shape appearance, (2) estimates their colors, and (3) detects special targets, from images of color video cameras set up toward a street. The categories of targets were classified into {human, sedan, van, truck, mule (golf cart for workers), and others), and their colors were classified into the groups of {redorange-yellow, green, blue-lightblue, white-silver-gray, darkblue-darkgray-black, and darkred-darkorange). On the detection of special targets, the test was carried out setting {FedEx van, UPS van, Police Car) as target and yielded desirable results. The system tracks the target, independently conducts category classification and color estimation, extracts the result with the largest probability throughout the tracking sequence from each result, and provides the data as the final decision. For classification and special target detection, we cooperatively used a stochastic linear discrimination method (linear discriminant analysis : LDA) and nonlinear decision rule (K-Nearest Neighbor rule: K-NN).

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Cite this paper

@inproceedings{Hasegawa2002AVS, title={A Vision System that Recognizes Objects on General Streets}, author={Osamu Hasegawa and Takeo Kanade}, booktitle={MVA}, year={2002} }