Abstract
With wide application of deep learning in security field, using it on vehicle brand, style and years recognition product has become an active research. Due to the variety of vehicle brand, the total quantity of training samples needed by deep learning is so huge that the difficulty of sample collection and corresponding cost on time and labor are both unacceptable. In addition, new vehicle types come out continuously which require database augmentation and product update in time. To solve this problem, this article proposes a vehicle model data classification algorithm based on hierarchy clustering. Firstly, train the classification model with vehicle data collected by the index of vehicle model information. Secondly, get mean feature of each class and use hierarchical clustering according to the distance between the classes. Then on the basis of distance sorting and model test result to merge the vehicle models. Finally, the feasibility of this algorithm is verified through the experiment. Experimental results show the scheme is feasible. The algorithm realizes the automatic clustering of vehicle model data whose car face or tail has the same structure which can’t be distinguish in image or video. This article provides a new way for the development of vehicle brand, style and year recognition products.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bo, P., Di, Z.G.: Vehicle logo recognition based on deep learning. Comput. Sci. 42(4), 268–273 (2015)
Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1891–1898. IEEE (2014)
Kang, K., Li, H., Yan, J., et al.: T-cnn: tubelets with convolutional neural networks for object detection from videos. arXiv preprint arXiv:1604.02532 (2016)
Junyu, G., Xiaoshan, Y., Tiazhu, Z., et al.: Robust visual tracking method via deep learning. Chin. J. Comput. 39(7), 1419–1434 (2016)
Wang, L., Ouyang, W., Wang, X., et al.: Visual tracking with fully convolutional networks. In: IEEE International Conference on Computer Vision, pp. 3119–3127. IEEE (2016)
Felzenszwalb, P.F., Girshick, R.B., Mcallester, D., et al.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)
Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8 (2008)
Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Computer Science, pp. 580–587 (2014)
Ren, S., He, K., Girshick, R., et al. Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Szegedy, C., Vanhoucke, V., Ioffe, S., et al.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Acknowledgement
The authors of this paper are members of Shanghai Engineering Research Center of Intelligent Video Surveillance. Our research was sponsored by following projects: the National Natural Science Foundation of China (61403084, 61402116); Program of Science and Technology Commission of Shanghai Municipality (Nos. 15530701300, 15XD15202000); 2012 IoT Program of Ministry of Industry and Information Technology of China; Key Project of the Ministry of Public Security (No. 2014JSYJA007); the Project of the Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University(ESSCKF 2015-03); Shanghai Rising-Star Program (17QB1401000).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Zhao, Y., Shao, J., Li, D., Mei, L. (2018). A Vehicle Model Data Classification Algorithm Based on Hierarchy Clustering. In: Abawajy, J., Choo, KK., Islam, R. (eds) International Conference on Applications and Techniques in Cyber Security and Intelligence. ATCI 2017. Advances in Intelligent Systems and Computing, vol 580. Edizioni della Normale, Cham. https://doi.org/10.1007/978-3-319-67071-3_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-67071-3_24
Published:
Publisher Name: Edizioni della Normale, Cham
Print ISBN: 978-3-319-67070-6
Online ISBN: 978-3-319-67071-3
eBook Packages: EngineeringEngineering (R0)