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Vehicle Color Recognition with Vehicle-Color Saliency Detection and Dual-Orientational Dimensionality Reduction of CNN Deep Features

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Abstract

Color is one of the most stable attributes of vehicles and often used as a valuable cue in some important applications. Various complex environmental factors, such as illumination, weather, noise and etc., result in the visual characteristics of the vehicle color being obvious diversity. Vehicle color recognition in complex environments has been a challenging task. The state-of-the-arts methods roughly take the whole image for color recognition, but many parts of the images such as car windows; wheels and background contain no color information, which will have negative impact on the recognition accuracy. In this paper, a novel vehicle color recognition method using local vehicle-color saliency detection and dual-orientational dimensionality reduction of convolutional neural network (CNN) deep features has been proposed. The novelty of the proposed method includes two parts: (1) a local vehicle-color saliency detection method has been proposed to determine the vehicle color region of the vehicle image and exclude the influence of non-color regions on the recognition accuracy; (2) dual-orientational dimensionality reduction strategy has been designed to greatly reduce the dimensionality of deep features that are learnt from CNN, which will greatly mitigate the storage and computational burden of the subsequent processing, while improving the recognition accuracy. Furthermore, linear support vector machine is adopted as the classifier to train the dimensionality reduced features to obtain the recognition model. The experimental results on public dataset demonstrate that the proposed method can achieve superior recognition performance over the state-of-the-arts methods.

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Acknowledgements

The work in this paper is supported by the National Natural Science Foundation of China (Nos. 61531006, 61372149, 61370189, 61471013, and 61602018), the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions (Nos. CIT&TCD20150311, CIT&TCD201404043), the Beijing Natural Science Foundation (Nos. 4142009, 4163071), the Science and Technology Development Program of Beijing Education Committee (Nos. KM201410005002, KM201510005004), Funding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality.

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Authors and Affiliations

  1. Signal and Information Processing Lab, Beijing University of Technology, Beijing, China

    Qiang Zhang, Jiafeng Li, Li Zhuo, Hui Zhang & Xiaoguang Li

  2. Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing, China

    Li Zhuo

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  1. Qiang Zhang
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  2. Jiafeng Li
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  3. Li Zhuo
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  4. Hui Zhang
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  5. Xiaoguang Li
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Correspondence to Li Zhuo.

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Zhang, Q., Li, J., Zhuo, L. et al. Vehicle Color Recognition with Vehicle-Color Saliency Detection and Dual-Orientational Dimensionality Reduction of CNN Deep Features. Sens Imaging 18, 20 (2017). https://doi.org/10.1007/s11220-017-0173-8

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  • DOI: https://doi.org/10.1007/s11220-017-0173-8

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