Abstract
In recent years, fine-grained vehicle recognition has been one of the essential tasks in Intelligent Traffic System (ITS) and has a multitude of applications, such as highway toll, parking intelligent management and vehicle safety monitoring. Fine-grained vehicle recognition is a challenging problem because of small inter-class distance and substantial sub-classes. To tackle this task, we propose a part-based model for fine-grained vehicle recognition in a weakly unsupervised manner. We also provide a part location method that locates the discriminative parts based on saliency maps which can be easily obtained by a single back-propagation pass. The advantage of the method is that the resolution of saliency maps is the same as the resolution of input images. Thus, we can locate discriminative parts efficiently and accurately. Additionally, we combine the whole-level features and part-level features and improve the accuracy of recognition up to 98.41% over 281 vehicle models in the large-scale dataset CompCars.
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References
Luo, J., Wu, J.: A survey on fine-grained image categorization using deep convolutional features. Zidonghua Xuebao/acta Automatica Sinica 43(8), 1306–1318 (2010)
Zhang, S., Zhan, Z.: Research on vehicle classification system based on SIFT features and support vector machine. Comput. Knowl. Technol. 8(17), 4277–4280 (2012)
Psyllos, A.P.: Anagnostopoulos: vehicle logo recognition using a sift-based enhanced matching scheme. IEEE Trans. Intell. Transp. Syst. 11(2), 322–328 (2010)
Petrovi, C.V.: Analysis of features for rigid structure vehicle type recognition. In: British Machine Vision Conference, pp. 587–596 (2004)
Zhang, B.: Reliable classification of vehicle types based on cascade classifier ensembles. IEEE Trans. Intell. Transp. Syst. 14(1), 322–332 (2013)
Donahue, J., Jia, Y., Vinyals, O.: Decaf: a deep convolutional activation feature for generic visual recognition. In: International Conference on Machine Learning, pp. 647–655 (2014)
Liu, M., Yu, C., Ling, H., Lei, J.: Hierarchical joint CNN-based models for fine-grained cars recognition. In: International Conference on Cloud Computing and Security, pp. 337–347 (2016)
Gao, Y., Lee, H.J.: Vehicle make recognition based on convolutional neural network. In: 2015 2nd International Conference on Information Science and Security (ICISS), pp. 1–4. IEEE (2015)
Fang, J., Zhou, Y., Yu, Y., Du, S.: Fine-grained vehicle model recognition using a coarse-to-fine convolutional neural network architecture. IEEE Trans. Intell. Transp. Syst. 18(7), 1782–1792 (2017)
Zhang, N., Donahue, J., Girshick, R., Darrell, T.: Part-based R-CNNs for fine-grained category detection. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 834–849. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_54
Wei, X.S., Xie, C.W.: Mask-CNN: localizing parts and selecting descriptors for fine-grained bird species categorization. Pattern Recognit. 76, 704–714 (2018)
Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640 (2017)
Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Yang, L., Luo, P., Chen, C.L., Tang, X.: A large-scale car dataset for fine-grained categorization and verification. In: Computer Vision and Pattern Recognition, pp. 3973–3981 (2015)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.: Imagenet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 248–255 (2009)
Acknowledgments
This work was supported by Research and Industrialization for Intelligent Video Processing Technology based on GPUs Parallel Computing of the Science and Technology Supported Program of Jiangsu Province (BY2016003-11) and the Application platform and Industrialization for efficient cloud computing for Big data of the Science and Technology Supported Program of Jiangsu Province (BA2015052).
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Zhou, Y., Yuan, J., Tang, X. (2018). A Novel Part-Based Model for Fine-Grained Vehicle Recognition. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11066. Springer, Cham. https://doi.org/10.1007/978-3-030-00015-8_56
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DOI: https://doi.org/10.1007/978-3-030-00015-8_56
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