15 February 2012 Application of the SNoW machine learning paradigm to a set of transportation imaging problems
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Abstract
Machine learning methods have been successfully applied to image object classification problems where there is clear distinction between classes and where a comprehensive set of training samples and ground truth are readily available. The transportation domain is an area where machine learning methods are particularly applicable, since the classification problems typically have well defined class boundaries and, due to high traffic volumes in most applications, massive roadway data is available. Though these classes tend to be well defined, the particular image noises and variations can be challenging. Another challenge is the extremely high accuracy typically required in most traffic applications. Incorrect assignment of fines or tolls due to imaging mistakes is not acceptable in most applications. For the front seat vehicle occupancy detection problem, classification amounts to determining whether one face (driver only) or two faces (driver + passenger) are detected in the front seat of a vehicle on a roadway. For automatic license plate recognition, the classification problem is a type of optical character recognition problem encompassing multiple class classification. The SNoW machine learning classifier using local SMQT features is shown to be successful in these two transportation imaging applications.
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Peter Paul, Peter Paul, Aaron M. Burry, Aaron M. Burry, Yuheng Wang, Yuheng Wang, Vladimir Kozitsky, Vladimir Kozitsky, } "Application of the SNoW machine learning paradigm to a set of transportation imaging problems", Proc. SPIE 8305, Visual Information Processing and Communication III, 830512 (15 February 2012); doi: 10.1117/12.912110; https://doi.org/10.1117/12.912110
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