Classify vehicles in traffic scene images with deformable part-based models
- 113 Downloads
Vehicle classification is an important and challenging task in intelligent transportation systems, which has a wide range of applications. In this paper, we propose to integrate vehicle detection and vehicle classification into one single framework by using deformable part-based models. First of all, we use annotated vehicle images to train a deformable part-based model for each class of vehicles to be classified. Then, given a traffic scene image, we employ the obtained vehicle models to perform vehicle detection in it for vehicle extraction. After that, model alignment is performed on the extracted image crop, based on which features are extracted for creating a representation for the vehicle in the given image. We train a linear multi-class Support Vector Machine classifier based on representations of images in a validation set. Finally, we adopt the SVM classifier for vehicle classification. The proposed method is evaluated on the BIT-Vehicle Dataset, and can achieve an accuracy of \(91.08\%\), which is superior to methods used for comparison. Obtained results demonstrated the effectiveness of the proposed method.
KeywordsVehicle detection Vehicle classification Deformable part-based model Support Vector Machine Appearance feature
This work was supported in part by National Natural Science Foundation of China (61602027).
- 2.Chang, C.-C., Lin C.-J: Libsvm: A library for support vector machines. (2001)Google Scholar
- 4.Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of Computer Vision and Pattern Recognition, pp. 886–893, (2005)Google Scholar
- 6.Farfade, S.S., Saberian, M., Li, L.-J: Multi-view face detection using deep convolutional neural networks. In: Proceedings of International Conference on Multimedia Retrieval, (2015)Google Scholar
- 8.Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (2014)Google Scholar
- 11.Jiang, M., Li, H.: Vehicle classification based on hierarchical support vector machine. In: Proceedings of the 2013 International Conference on Computer Engineering and Network, pp. 593–600, (2014)Google Scholar
- 13.Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105 (2012)Google Scholar
- 14.Lai, A.H.S., Fung, G.S.K., Yung, N.H.C.: Vehicle type classification from visual-based dimension estimation. In Proceedings of IEEE International Conference on Intelligent Transportation Systems, pp. 201–206, (2001)Google Scholar
- 16.Ma, X., Grimson, W.: Edge-based rich representation for vehicle classification. In: IEEE International Conference on Computer Vision, pp. 1185–1192, (2005)Google Scholar
- 18.Peng, Y., Jin, J.S., Luo, S., Xu, M., Au, S., Zhang, Z., Cui, Y.: Vehicle type classification using data mining techniques. In: The Era of Interactive Media, pp. 325–335, (2013)Google Scholar
- 19.Petrovic, V.S., Cootes, T.F: Analysis of features for rigid structure vehicle type recognition. In: British Machine Vision Conference, pp. 1–10Google Scholar
- 24.Szegedy, C., Liu, W., Jia, Y.Q., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9, (2015)Google Scholar