Abstract
Researches show in the test phase of Convolutional Neural Network (CNN), the most time cost occurred in convolutional layers, while the most memory cost occurred in the fully connected layers. With the rapid development of the pedestrian methods, which are based on deep learning, the performance is going better and better. Especially using resemble models, pedestrian detection can get a more excellent performance. However, the performance is improved by the increase in parameters and slow of speed in price. Meanwhile, for some specific tasks, such as driverless cars, due to the limitations of hardware facilities, it is impossible to use these methods on them. In this paper, we applied the tensor decomposition to pedestrian detection task, in order to accelerate the whole pedestrian detection processing. Experiments show even though the decomposition brings some of the rise of miss rate (MR), the saved memory and time indicates the efficiency of our method.
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
Yao, Q., An, S.Q., Yao, L.: Algorithms of pedestrian detection and tracking based on three frame different method and mean-shift algorithm. Comput. Eng. Des. 35(1), 223–227 (2014). (in Chinese)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893. IEEE Computer Society (2005)
Xia, D., Sun, H., Shen, Z.: Real-time infrared pedestrian detection based on multi-block LBP. In: 2010 International Conference on Computer Application and System Modeling (ICCASM), vol. 12, pp. V12-139–V12-142. IEEE (2010)
Felzenszwalb, P., Mcallester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–8. DBLP (2008)
Li, J., Wu, Y., Zhao, J., et al.: Pedestrian detection with dilated convolution, region proposal network and boosted decision trees. In: The 2017 International Joint Conference on Neural Networks (IJCNN 2017), pp. 4052–4057 (2017)
Yang, B., Yan, J., Lei, Z., et al.: Convolutional channel features. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 82–90 (2015)
Tian, Y., Luo, P., Wang, X., et al.: Deep learning strong parts for pedestrian detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1904–1912 (2015)
Cai, Z., Saberian, M., Vasconcelos, N.: Learning complexity-aware cascades for deep pedestrian detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3361–3369 (2015)
Cai, Z., Fan, Q., Feris, R.S., et al.: A unified multi-scale deep convolutional neural network for fast object detection. In: European Conference on Computer Vision, pp. 354–370. Springer International Publishing, Heidelberg (2016)
Zhang, L., Lin, L., Liang, X., et al.: Is faster R-CNN doing well for pedestrian detection? In: European Conference on Computer Vision, pp. 443–457 (2016)
Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. Comput. Sci. 14(7), 38–39 (2015)
Romero, A., Ballas, N., Kahou, S.E., et al.: FitNets: hints for thin deep nets. arXiv preprint arXiv:1412.6550 (2014)
Shen, J., Vesdapunt, N., Boddeti, V.N., et al.: In Teacher We Trust: Learning Compressed Models for Pedestrian Detection. arXiv preprint arXiv:1612.00478 (2016)
Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural networks with pruning, trained quantization and Huffman coding. Fiber 56(4), 3–7 (2015)
Rigamonti, R., Sironi, A., Lepetit, V., et al.: Learning separable filters. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2754–2761. IEEE Computer Society (2013)
Jaderberg, M., Vedaldi, A., Zisserman, A.: Speeding up convolutional neural networks with low rank expansions. arXiv preprint arXiv:1405.3866 (2014)
Denton, E., Zaremba, W., Bruna, J., et al.: Exploiting linear structure within convolutional networks for efficient evaluation. In: Advances in Neural Information Processing Systems, pp. 1269–1277 (2014)
Lebedev, V., Ganin, Y., Rakhuba, M., et al.: Speeding-up convolutional neural networks using fine-tuned CP-decomposition. In: Proceedings of the International Conference Learn, Represent (2015)
Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Computer Vision and Pattern Recognition, pp. 580–587. IEEE (2014)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards realtime object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Du, X., El-Khamy, M., Lee, J., et al.: Fused DNN: a deep neural network fusion approach to fast and robust pedestrian detection. arXiv preprint arXiv:1610.03466 (2016)
Liu, W., Anguelov, D., Erhan, D., et al.: SSD: Single Shot MultiBox Detector. arXiv preprint arXiv:1512.02325 (2015)
Dollar, P., Wojek, C., Schiele, B., et al.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34(4), 743–761 (2012)
Deng, J., Dong, W., Socher, R., et al.: Imagenet: a large-scale hierarchical image database. In: Computer Vision and Pattern Recognition, pp. 248–255 (2009)
Sorber, L., Van, B.M., De, L.: Tensorlab v2.0 (2014). http://tensorlab.net
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint arXiv:1312.4400 (2013)
Piotr, D.: Piotr’s Computer Vision Matalab Toolbox (PMT). https://github.com/pdollar/toolbox
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Wu, Y., Jiang, W., Li, J., Yang, T. (2017). Speeding Up Dilated Convolution Based Pedestrian Detection with Tensor Decomposition. In: Huang, DS., Hussain, A., Han, K., Gromiha, M. (eds) Intelligent Computing Methodologies. ICIC 2017. Lecture Notes in Computer Science(), vol 10363. Springer, Cham. https://doi.org/10.1007/978-3-319-63315-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-63315-2_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63314-5
Online ISBN: 978-3-319-63315-2
eBook Packages: Computer ScienceComputer Science (R0)