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Hybrid model of clustering and kernel autoassociator for reliable vehicle type classification

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Abstract

Automatic vehicle classification is an important area of research for intelligent transportation, traffic surveillance and security. A working image-based vehicle classification system is proposed in this paper. The first component vehicle detection is implemented by applying histogram of oriented gradient features and SVM classifier. The second component vehicle classification, which is the emphasis of this paper, is accomplished by a hybrid model composed of clustering and kernel autoassociator (KAA). The KAA model is a generalization of auto-associative networks by training to recall the inputs through kernel subspace. As an effective one-class classification strategy, KAA has been proposed to implement classification with rejection, showing balanced error–rejection trade-off. With a large number of training samples, however, the training of KAA becomes problematic due to the difficulties involved with directly creating the kernel matrix. As a solution, a hybrid model consisting of self-organizing map (SOM) and KAM has been proposed to first acquire prototypes and then construct the KAA model, which has been proven efficient in internet intrusion detection. The hybrid model is further studied in this paper, with several clustering algorithms compared, including k-mean clustering, SOM and Neural Gas. Experimental results using more than 2,500 images from four types of vehicles (bus, light truck, car and van) demonstrated the effectiveness of the hybrid model. The proposed scheme offers a performance of accuracy over \(95~\%\) with a rejection rate \(8~\%\) and reliability over \(98~\%\) with a rejection rate of \(20~\%\). This exhibits promising potentials for real-world applications.

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Acknowledgments

The project is funded by Suzhou Municipal Science And Technology Foundation Key Technologies for Video Objects Intelligent Analysis for Criminal Investigation (SS201109).

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Authors and Affiliations

  1. Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, People’s Republic of China

    Bailing Zhang, Yifan Zhou, Hao Pan & Tammam Tillo

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  1. Bailing Zhang
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  2. Yifan Zhou
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  3. Hao Pan
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  4. Tammam Tillo
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Correspondence to Bailing Zhang.

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Zhang, B., Zhou, Y., Pan, H. et al. Hybrid model of clustering and kernel autoassociator for reliable vehicle type classification. Machine Vision and Applications 25, 437–450 (2014). https://doi.org/10.1007/s00138-013-0588-8

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  • DOI: https://doi.org/10.1007/s00138-013-0588-8

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