Abstract
In this paper, we propose a method for counting motorcycles from images based on the selective search and deep learning. In the proposed approach, the objects in an image are segmented by the selective searching algorithm and then recognized individually by the Convolutional Neural Network (CNN). Finally, the objects recognized as motorcycles are counted. The experimental results show the proposed method is effective in counting the motorcycles.
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References
Bruno, A., Greco, L., La Cascia, M.: Video object recognition and modeling by SIFT matching optimization. In: Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods (2014)
Chung, J., Sohn, K.: Image-based learning to measure traffic density using a deep convolutional neural network. IEEE Trans. Intell. Transp. Syst. 19(5), 1670–1675 (2018)
Kato, T., Ninomiya, Y., Masaki, I.: Preceding vehicle recognition based on learning from sample images. IEEE Trans. Intell. Transp. Syst. 3(4), 252–260 (2002)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, IPS 2012, USA, vol. 1, pp. 1097–1105. Curran Associates Inc. (2012)
Matthews, N., An, P., Charnley, D., Harris, C.: Vehicle detection and recognition in greyscale imagery. Control Eng. Pract. 4(4), 473–479 (1996)
Mukhtar, A., Tang, T.B.: Vision based motorcycle detection using HOG features. In: Proceeding of 2015 IEEE International Conference on Signal and Image Processing Applications (2015)
Silva, R., Aires, K., Santos, T., Abdala, K., Veras, R., Soares, A.: Automatic detection of motorcyclists without helmet. In: Proceeding of 2013 XXXIX Latin American Computing Conference (2013)
Uijlings, J.R.R., van de Sande, K.E.A., Gevers, T., Smeulders, A.W.M.: Selective search for object recognition. Int. J. Comput. Vision 104(2), pp. 154–171 (2013)
Wen, X., Yuan, H., Song, C., Liu, W., Zhao, H.: An algorithm based on SVM ensembles for motorcycle recognition. In: Proceeding of 2007 IEEE International Conference on Vehicular Electronics and Safety (2007)
Zhang, Y., Pezeshki, M., Brakel, P., Zhang, S., Laurent, C., Bengio, Y., Courville, A.:Towards end-to-end speech recognition with deep convolutional neural networks. In: Interspeech, pp. 410–414 (2016)
Rahman, M.A., Wang, Y.: Optimizing intersection-over-union in deep neural networks for image segmentation. In: The International Symposium on Visual Computing, pp. 234–244 (2016)
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Hong, TP., Yang, YC., Su, JH. (2019). Using Selective Search and CNN for Counting Motorcycles in Images. In: Zhao, Y., Wu, TY., Chang, TH., Pan, JS., Jain, L. (eds) Advances in Smart Vehicular Technology, Transportation, Communication and Applications. VTCA 2018. Smart Innovation, Systems and Technologies, vol 128. Springer, Cham. https://doi.org/10.1007/978-3-030-04585-2_37
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DOI: https://doi.org/10.1007/978-3-030-04585-2_37
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