Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Convolutional Neural Network-based System for Vehicle Front-Side Detection

컨볼루션 신경망 기반의 차량 전면부 검출 시스템

  • Park, Young-Kyu (School of Mechanical Engineering, Pusan National University) ;
  • Park, Je-Kang (School of Mechanical Engineering, Pusan National University) ;
  • On, Han-Ik (School of Mechanical Engineering, Pusan National University) ;
  • Kang, Dong-Joong (School of Mechanical Engineering, Pusan National University)
  • Received : 2015.08.24
  • Accepted : 2015.10.23
  • Published : 2015.11.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes a method for detecting the front side of vehicles. The method can find the car side with a license plate even with complicated and cluttered backgrounds. A convolutional neural network (CNN) is used to solve the detection problem as a unified framework combining feature detection, classification, searching, and localization estimation and improve the reliability of the system with simplicity of usage. The proposed CNN structure avoids sliding window search to find the locations of vehicles and reduces the computing time to achieve real-time processing. Multiple responses of the network for vehicle position are further processed by a weighted clustering and probabilistic threshold decision method. Experiments using real images in parking lots show the reliability of the method.

Keywords

References

  1. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," Proc. Computer Vision and Pattern Recognition, pp. 886-893, 2005.
  2. D. G. Lowe, "Distinctive image features from scaleinvariant keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  3. Freund, Yoav, and Robert E, Schapire. "Experiments with a new boosting algorithm." International Conference on Machine Learning, vol. 96. pp. 148-156, 1996.
  4. C. Cortes and V. Vapnik, "Support vector networks," Mach. Learn., vol. 20, pp. 273-297, 1995.
  5. J. Uijlings, K. van de Sande, T. Gevers, and A. Smeulders, "Selective search for object recognition," International Journal of Computer Vision, vol. 104, no. 2, pp. 154-171, 2013. https://doi.org/10.1007/s11263-013-0620-5
  6. P. Viola and M. Jones, "Robust real-time face detection," International Journal of Computer Vision, vol. 57, no. 2, pp. 137-154, 2004. https://doi.org/10.1023/B:VISI.0000013087.49260.fb
  7. P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan, "Object detection with discriminatively trained part based models," Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1627-1645, 2010. https://doi.org/10.1109/TPAMI.2009.167
  8. G. Hinton, S. Osindero, and Y. Teh, "A fast learning algorithm for deep belief nets," Neural Computation, vol. 18, pp. 1527-1554, 2006. https://doi.org/10.1162/neco.2006.18.7.1527
  9. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proc. of IEEE, vol. 86, no. 11, pp. 2278-2324, 1998. https://doi.org/10.1109/5.726791
  10. A. Krizhevsky, I. Sutskever, and G. Hinton. "ImageNet classification with deep convolutional neural networks," Advances in Neural Information Processing Systems, pp. 1097-1105, 2012.
  11. P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, "OverFeat: Integrated recognition, localization and detection using convolutional networks," Proc. of International Conference on Learning Representations, 2014.
  12. Y. Jia, "Caffe: An open source convolutional architecture for fast feature embedding," http://caffe.berkeleyvision.org, 2013.
  13. Comaniciu, Dorin, and Peter Meer, "Mean shift: A robust approach toward feature space analysis," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 24, no. 5, pp. 603-619, 2002. https://doi.org/10.1109/34.1000236
  14. I. S. Cho, J. H. Lee, and S. J. Oh, "Development of the flexible user-friendly real-time machine vision inspection system," Journal of the Institute of Electronics Engineers of Korea, vol. 45, no. 3, pp. 42-50, May 2008.
  15. I. S. Oh, "Pattern recognition," Kyobobook, 2008.
  16. S. M. Yang and K. H. Jo. "HOG based pedestrian detection and behavior pattern recognition for traffic signal control," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 19, no. 11, pp. 1017-1021, 2013. https://doi.org/10.5302/J.ICROS.2013.13.1858
  17. S. W. Lee, J. W. Jang, and K. R. Baek, "Pedestrian detection algorithm using a gabor filter bank," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 20, no. 9, pp. 930-935, 2014. https://doi.org/10.5302/J.ICROS.2014.13.0020

Cited by

  1. Fast Deep Neural Networks With Knowledge Guided Training and Predicted Regions of Interests for Real-Time Video Object Detection vol.6, pp.2169-3536, 2018, https://doi.org/10.1109/ACCESS.2018.2795798