Abstract
Fine-grained vehicle recognition in traffic surveillance plays a crucial part in establishing intelligent transportation system. The major challenge lies in that differences among vehicle models are always subtle. In this paper, we propose a part-based method combining global and local feature for fine-grained vehicle recognition in traffic surveillance. We develop a novel voting mechanism to unify the preliminary recognition results, which are obtained by using Histograms of Oriented Gradients (HOG) and pre-trained convolutional neural networks (CNN), leading to fully exploiting the discriminative ability of different parts. Besides, we collect a comprehensive public database for 50 common vehicle models with manual annotation of parts, which is used to evaluate the proposed method and serves as supportive dataset for related work. The experiments show that the average recognition accuracy of our method can approach 92.3 %, which is 3.4 %–7.1 % higher than the state-of-art approaches.
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Acknowledgements
This work was partly supported by the National Natural Science Foundation of China (61502348), the EU FP7 QUICK project under Grant Agreement No. PIRSES-GA-2013-612652, China Postdoctoral Science Foundation funded project (2014M562058), Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry ([2014]1685), the Fundamental Research Funds for the Central Universities (2042016gf0033), Natural Science Fund of Hubei Province (2015CFB406).
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Wang, Q., Wang, Z., Xiao, J., Xiao, J., Li, W. (2016). Fine-Grained Vehicle Recognition in Traffic Surveillance. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9916. Springer, Cham. https://doi.org/10.1007/978-3-319-48890-5_28
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DOI: https://doi.org/10.1007/978-3-319-48890-5_28
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