Browse > Article
http://dx.doi.org/10.5909/JBE.2019.24.2.357

Deep Learning-Based Real-Time Pedestrian Detection on Embedded GPUs  

Vien, An Gia (Pukyong National University, Department of Computer Engineering)
Lee, Chul (Pukyong National University, Department of Computer Engineering)
Publication Information
Journal of Broadcast Engineering / v.24, no.2, 2019 , pp. 357-360 More about this Journal
Abstract
We propose an efficient single convolutional neural network (CNN) for pedestrian detection on embedded GPUs. We first determine the optimal number of the convolutional layers and hyper-parameters for a lightweight CNN. Then, we employ a multi-scale approach to make the network robust to the sizes of the pedestrians in images. Experimental results demonstrate that the proposed algorithm is capable of real-time operation, while providing higher detection performance than conventional algorithms.
Keywords
Pedestrian detection; convolutional neural network; embedded system;
Citations & Related Records
연도 인용수 순위
  • Reference
1 P. Dollar, C. Wojek, B. Schiele, and P. Perona, “Pedestrian detection: An Evaluation of The State of The Art,” IEEE Transaction Pattern Analysis and Machine Intelligence, Vol. 34, No. 4, pp. 743-761, April 2012.   DOI
2 P. Sermanet, K. Kavukcuoglu, S. Chintala, and Y. Lecun, "Pedestrian Detection with Unsupervised Multi-Stage Feature Learning," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3626-3633, June 2013.
3 P. Dollar, R. Appel, S. Belongie, and P. Perona, “Fast Feature Pyramids for Object Detection,” IEEE Transaction Pattern Analysis and Machine Intelligence, Vol. 36, No. 8, pp. 1532-1545, August 2014.   DOI
4 X. Wang, T. X. Han, and S. Yan, "An HOG-LBP Human Detector with Partila Occlusion Handling," Proceeding of IEEE Conference on Computer Vision, pp. 32-39, September 2009.
5 S. Zhang, C. Bauckhage, and A. B. Cremers, "Informed Haar-Like Features Improve Pedestrian Detection," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 947-954, June 2014.
6 K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," Proceedings of International Conference on Learning and Representations, May 2015.
7 C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, "Going Deeper with Convolutions," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9, June 2015.
8 K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, June 2016.
9 J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788, June 2016.
10 J. Redmon and A. Farhadi, "YOLO9000: Better, Faster, Stronger," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263-7271, July 2017.
11 S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," Proceeding International Conference on Neural Information Processing Systems, pp. 91-99, December 2015.
12 A. L. Maas, A. Y. Hannun, and A. Y. Ng, "Rectifier Nonlinearities Improve Neural Network Acoustic Models," International Conference on Machine Learning Workshop on Deep Learning for Audio, Speech, and Language, 2013.
13 S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift," Proceeding of International Conference on Machine Learning, pp. 448-456, July 2015.
14 J. Redmon, "Darknet: Open Source Neural Network in C," http://pjreddie.com/darknet/, 2013-2016.