Table of Contents Author Guidelines Submit a Manuscript
Applied Computational Intelligence and Soft Computing
Volume 2017 (2017), Article ID 9830641, 13 pages
https://doi.org/10.1155/2017/9830641
Research Article

Multiscale Convolutional Neural Networks for Hand Detection

1Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
2Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, UK

Correspondence should be addressed to Shiyang Yan; nc.ude.ultjx@nay.gnayihs

Received 10 November 2016; Revised 19 January 2017; Accepted 2 April 2017; Published 22 May 2017

Academic Editor: Xinzheng Xu

Copyright © 2017 Shiyang Yan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '01), vol. 1, pp. I–511–I–518, 2001. View at Scopus
  2. N. H. Dardas and N. D. Georganas, “Real-time hand gesture detection and recognition using bag-of-features and support vector machine techniques,” Instrumentation and Measurement, IEEE Transactions on, vol. 60, no. 11, pp. 3592–3607, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), vol. 1, pp. 886–893, June 2005. View at Publisher · View at Google Scholar · View at Scopus
  4. X. Meng, J. Lin, and Y. Ding, “An extended hog model: schog for human hand detection,” in Proceedings of the International Conference on Systems and Informatics (ICSAI '12), pp. 2593–2596, China, May 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. P. Dollar, R. Appel, S. Belongie, and P. Perona, “Fast feature pyramids for object detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 8, pp. 1532–1545, 2014. View at Publisher · View at Google Scholar
  6. N. Das, E. Ohn-Bar, and M. M. Trivedi, “On performance evaluation of driver hand detection algorithms: challenges, dataset, and metrics,” in Proceedings of the 18th IEEE International Conference on Intelligent Transportation Systems (ITSC '15), pp. 2953–2958, Spain, September 2015. View at Publisher · View at Google Scholar · View at Scopus
  7. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1627–1645, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. A. Mittal, A. Zisserman, and P. Torr, “Hand detection using multiple proposals,” in Proceedings of the British Machine Vision Conference (BMVC '11), pp. 1–11, Dundee, UK, 2011. View at Publisher · View at Google Scholar
  9. S. Bambach, S. Lee, D. J. Crandall, and C. Yu, “Lending a hand: detecting hands and recognizing activities in complex egocentric interactions,” in Proceedings of the 15th IEEE International Conference on Computer Vision, (ICCV '15), pp. 1949–1957, Chile, December 2015. View at Publisher · View at Google Scholar · View at Scopus
  10. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified real-time object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788, 2016.
  11. T. Zhou, P. J. Pillai, and V. G. Yalla, “Hierarchical context-aware hand detection algorithm for naturalistic driving,” in Proceedings of the IEEE 19th International Conference on Intelligent Transportation Systems (ITSC '16), pp. 1291–1297, Rio de Janeiro, Brazil, November 2016. View at Publisher · View at Google Scholar
  12. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proceedings of the 26th Annual Conference on Neural Information Processing Systems (NIPS '12), pp. 1097–1105, Lake Tahoe, Nev, USA, December 2012. View at Scopus
  13. O. Russakovsky, J. Deng, H. Su et al., “Imagenet large scale visual recognition challenge,” International Journal of Computer Vision, vol. 115, no. 3, pp. 211–252, 2015. View at Publisher · View at Google Scholar · View at MathSciNet
  14. R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Region-based convolutional networks for accurate object detection and segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 1, pp. 142–158, 2016. View at Publisher · View at Google Scholar · View at Scopus
  15. M. Everingham, L. van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (VOC) challenge,” International Journal of Computer Vision, vol. 88, no. 2, pp. 303–338, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. T.-Y. Lin, M. Maire, S. Belongie et al., “Microsoft COCO: Common objects in context,” Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8693, no. 5, pp. 740–755, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. R. Girshick, “Fast R-CNN,” in Proceedings of the 15th IEEE International Conference on Computer Vision (ICCV '15), pp. 1440–1448, December 2015. View at Publisher · View at Google Scholar · View at Scopus
  18. K. He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 9, pp. 1904–1916, 2015. View at Publisher · View at Google Scholar · View at Scopus
  19. Z. Cai, Q. Fan, R. S. Feris, and N. Vasconcelos, “A unified multi-scale deep convolutional neural network for fast object detection,” in Proceedings of the European Conference on Computer Vision, pp. 354–370, Springer, Berlin, Germany, 2016.
  20. M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in Proceedings of the Computer Vision—ECCV 2014: 13th European Conference, vol. 8689 of Lecture Notes in Computer Science, pp. 818–833, Springer, Zurich, Switzerland, September 6–12, 2014. View at Publisher · View at Google Scholar
  21. S. Yang and D. Ramanan, “Multi-scale recognition with DAG-CNNs,” in 15th IEEE International Conference on Computer Vision, ICCV 2015, pp. 1215–1223, chl, December 2015. View at Publisher · View at Google Scholar · View at Scopus
  22. I. F. Ince, M. Socarras-Garzon, and T.-C. Yang, “Hand mouse: real time hand motion detection system based onanalysis of finger blobs,” International Journal of Digital Content Technology and Its Applications, vol. 4, no. 2, pp. 40–56, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. G.-Z. Mao, Y.-L. Wu, M.-K. Hor, and C.-Y. Tang, “Real-time hand detection and tracking against complex background,” in Proceedings of the 5th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP '09), pp. 905–908, Japan, September 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. V. Chouvatut, C. Yotsombat, R. Sriwichai, and W. Jindaluang, “Multi-view hand detection applying viola-jones framework using SAMME AdaBoost,” in Proceedings of the 7th International Conference on Knowledge and Smart Technology (KST '15), pp. 30–35, Thailand, January 2015. View at Publisher · View at Google Scholar · View at Scopus
  25. J. Zhu, H. Zou, S. Rosset, and T. Hastie, “Multi-class AdaBoost,” Statistics and Its Interface, vol. 2, no. 3, pp. 349–360, 2009. View at Publisher · View at Google Scholar · View at MathSciNet
  26. Y. LeCun, B. Boser, J. S. Denker et al., “Backpropagation applied to handwritten zip code recognition,” Neural Computation, vol. 1, no. 4, pp. 541–551, 1989. View at Publisher · View at Google Scholar
  27. Y. Bengio, A. Courville, and P. Vincent, “Representation learning: a review and new perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798–1828, 2013. View at Publisher · View at Google Scholar · View at Scopus
  28. C. Szegedy, A. Toshev, and D. Erhan, “Deep neural networks for object detection,” in Advances in Neural Information Processing Systems 26, C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, Eds., pp. 2553–2561, Curran Associates, Inc, Red Hook, NY, USA, 2013. View at Google Scholar
  29. R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '14), pp. 580–587, IEEE, Columbus, Ohio, USA, June 2014. View at Publisher · View at Google Scholar · View at Scopus
  30. K. E. A. Van De Sande, J. R. R. Uijlings, T. Gevers, and A. W. M. Smeulders, “Segmentation as selective search for object recognition,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV '11), pp. 1879–1886, IEEE, November 2011. View at Publisher · View at Google Scholar · View at Scopus
  31. S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: towards real-time object detection with region proposal networks,” in Advances in Neural Information Processing Systems, pp. 91–99, 2015. View at Google Scholar · View at Scopus
  32. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: unified, real-time object detection,” https://arxiv.org/abs/1506.02640.
  33. S. Bell, C. L. Zitnick, K. Bala, and R. Girshick, “Inside-outside net: detecting objects in context with skip pooling and recurrent neural networks,” 2015, https://arxiv.org/abs/1512.04143. View at Publisher · View at Google Scholar
  34. S. Zagoruyko, A. Lerer, T.-Y. Lin et al., “A multipath network for object detection,” https://arxiv.org/abs/1604.02135.
  35. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 2014, https://arxiv.org/abs/1409.1556.
  36. B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik, “Hypercolumns for object segmentation and fine-grained localization,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '15), pp. 447–456, Boston, Mass, USA, June 2015. View at Publisher · View at Google Scholar · View at Scopus
  37. W. Liu, A. Rabinovich, and A. C. Berg, “Parsenet: looking wider to see better,” https://arxiv.org/abs/1506.04579.
  38. Y. Jia, E. Shelhamer, J. Donahue et al., “Caffe: convolutional architecture for fast feature embedding,” in Proceedings of the ACM International Conference on Multimedia, pp. 675–678, ACM, Orlando, Fla, USA, November 2014. View at Publisher · View at Google Scholar
  39. C. L. Zitnick and P. Dollár, “Edge boxes: locating object proposals from edges,” in Proceedings of the European Conference on Computer Vision (ECCV '14), pp. 391–405, Springer, Zurich, Switzerland, September 2014. View at Publisher · View at Google Scholar
  40. J. Hosang, R. Benenson, P. Dollar, and B. Schiele, “What makes for effective detection proposals?” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 4, pp. 814–830, 2016. View at Publisher · View at Google Scholar · View at Scopus
  41. F. J. Provost, T. Fawcett, and R. Kohavi, “The case against accuracy estimation for comparing induction algorithms,” in Proceedings of the International Conference on Machine Learning (ICML '98), vol. 98, pp. 445–453, 1998.