Pedestrian Detection based on Faster R-CNN

doi:10.23940/ijpe.19.07.p5.17921801

Abstract

Abstract: Pedestrian detection has a wide range of applications, such as intelligent assisted driving, intelligent monitoring, pedestrian analysis, and intelligent robotics. Therefore, it has been the focus of research on target detection applications. In this paper, the Faster R-CNN target detection model is combined with the convolutional neural networks VGG16 and ResNet101 respectively, and the deep convolutional neural network is used to extract the image features. By adjusting the structure and parameters of Faster R-CNN's RPN, the multi-scale problem existing in the pedestrian detection process is solved to some extent. The experimental results compare the detection ability of the two schemes on the INRIA pedestrian dataset. The resulting model is migrated and validated on the Pascal Voc2007 dataset.

Key words: pedestrian detection, faster R-CNN, feature extraction, deep learning

Shuang Liu, Xing Cui, Jiayi Li, Hui Yang, and Niko Lukač. Pedestrian Detection based on Faster R-CNN [J]. Int J Performability Eng, 2019, 15(7): 1792-1801.

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References

[1] N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” in Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), Vol. 1, pp. 886-893, 2005
[2] K. Schindler, N. Majer, S. Walk, and B. Schiele, “New Features and Insights for Pedestrian Detection,” in Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1030-1037, 2010
[3] P. Dollar, S. Belongie,P. Perona, “The Fastest Pedestrian Detector in the West,” inProceedings of the British Machine Vision Conference, pp. 1-11, 2010
[4] P. Felzenszwalb, D. McAllester, and D. Ramanan, “A Discriminatively Trained, Multiscale, Deformable Part Model,” in Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition, Vol. 8, pp. 1-8, 2008
[5] S. Tang, M. Andriluka,B. Schiele, “Detection and Tracking of Occluded People,” International Journal of Computer Vision, Vol. 110, No. 1, pp. 58-69, 2014
[6] R. Girshick, J. Donahue, T. Darrell,J. Malik, “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,” inProceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 580-587, 2014
[7] K. He, X. Zhang, S. Ren,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
[8] R. Girshick, “Fast R-CNN,” in Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440-1448, 2015
[9] S. Ren, K. He, R. Girshick,J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, No. 6, pp. 1137-1149, 2017
[10] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv:1409.1556, 2014
[11] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, 2016
[12] W. Luo, Y. Li, R. Urtasun,R. Zemel, “Understanding the Effective Receptive Field in Deep Convolutional Neural Networks,” inProceedings of 30th Annual Conference on Neural Information Processing Systems (NIPS), pp. 4905-4913, 2016
[13] S. Wei, V. Ramakrishna, T. Kanade, and Y. Sheikh, “Convolutional Pose Machines,” in Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4724-4732, 2016
[14] M. Everingham, S. M. A.Eslami, L. J. V. Gool, C. K. I. Williams, J. M. Winn, and A. Zisserman, “The Pascal Visual Object Classes Challenge: A Retrospective,” International Journal of Computer Vision, Vol. 111, No. 1, pp. 98-136, 2015