Conference Interactive Program
To view the 2013 Annual Meeting Interactive Program
TRB 90th Annual Meeting (January 23-27, 2011)
Intelligent Transportation Systems Development and Applications
Jan 24 2011 7:30PM- 9:30PM
Marriott, Salon 2
Method for Preceding Vehicle Type Classification Based on Sparse Representation (11-1724)
Preceding vehicle recognition is an important enabling technology for developing a driving assistance system and an autonomous vehicle system. A new vehicle type classifier based on sparse representation model, is developed in this paper for classification of preceding vehicles. Unlike general supervised learning methods, where a training procedure is used to create a classification model for testing, the sparse representation approach does not contain separate training and testing stages. Instead, classification is achieved directly out of the testing sample¡¯s sparse representation in terms of training samples. Another unique feature of the new method is no model selection needed. The implementation of sparse representation classification for vehicle type (SRCVT) includes the following four steps: data preparation, principal component analysis (PCA) transformation, realization of SRCVT and classification output. Numerical experiments are designed to quantitatively verify the performance of SRCVT using the vehicle region of interest (ROI) data gotten by the preceding vehicle detection process. Experiment results show that the proposed SRCVT approach can outperform the best performance achieved by SVMs and has no need of model selection.
Chong, Yanwen , Wuhan University, China
Chen, Wu , Hong Kong Polytechnic University
Li, Zhilin , Hong Kong Polytechnic University
Lam, William H. K. , Hong Kong Polytechnic University
Transportation Research Board. 500 Fifth St. NW, Washington, D.C. 20001
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