NDLI: Reliable vehicle type classification by Classified Vector Quantization

Content Provider	IEEE Xplore Digital Library
Author	Bailing Zhang Yifan Zhou
Copyright Year	2012
Description	Author affiliation: Department of Computer Science and Software Eng., Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China (Bailing Zhang; Yifan Zhou)
Abstract	A working vehicle detection and classification system is proposed in this paper. The vehicle detection is implemented by a multiple layer perceptrons (MLP) ensemble using Haar-like features. To address the classification reliability issue, a prototype based scheme, called Classified Vector Quantization (CVQ), was applied for vehicle classification. By CVQ, each data category is represented by its own codebook, which can be implemented by some efficient neural learning algorithms, for example, the self-organizing map (SOM) and neural ‘gas’ algorithm. In classification process, each codebook offers a generalized ‘nearest neighbor’ by a population decoding principle to be compared with the input data. The advantage of CVQ is its convenience to provide reliable classification using the embedded rejection option. Experiments demonstrated the efficiency for vehicle classification task. The scheme offers a performance of accuracy over 95% with a rejection rate 8% and reliability over 98% with a rejection rate 20%. This exhibits promising potentials for real-world applications.
Starting Page	1148
Ending Page	1152
File Size	428768
Page Count	5
File Format	PDF
ISBN	9781467309653
e-ISBN	9781467309646
DOI	10.1109/CISP.2012.6469857
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Vehicle detection Clustering algorithms Vectors Reliability Image reconstruction Vehicles
Content Type	Text
Resource Type	Article

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