Pedestrian Recognition Method Based on Depth Hierarchical Feature Representation

SUN Rui; ZHANG Guanghai; GAO Jun

doi:10.11999/JEIT150982

Volume 38 Issue 6

Jun. 2016

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SUN Rui, ZHANG Guanghai, GAO Jun. Pedestrian Recognition Method Based on Depth Hierarchical Feature Representation[J]. Journal of Electronics & Information Technology, 2016, 38(6): 1528-1535. doi: 10.11999/JEIT150982

Citation:

SUN Rui, ZHANG Guanghai, GAO Jun. Pedestrian Recognition Method Based on Depth Hierarchical Feature Representation[J]. Journal of Electronics & Information Technology, 2016, 38(6): 1528-1535. doi: 10.11999/JEIT150982

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Pedestrian Recognition Method Based on Depth Hierarchical Feature Representation

doi: 10.11999/JEIT150982

Funds:

The National Natural Science Foundation of China (61471154), Scientific Research Foundation for Returned Scholars, Ministry of Education of China

Received Date: 2015-09-06
Rev Recd Date: 2015-12-25
Publish Date: 2016-06-19

Abstract

Abstract

For feature representation of pedestrian recognition, a hybrid hierarchical feature representation method which combines representation ability of the bag of words model and depth layered with learning adaptability is presented. This method first uses HOG local descriptor gradient-based for local features extraction, and then encoding the feature by a depth of layered coding method, the layered coding method by spatial aggregating Restricted Boltzmann Machine (RBM). For each coding layer, the sparse and selective regularization are used for the unsupervised RBM learning and supervision fine-tuning is used to enhance the visual features representation in classification task. Finally, high-level image feature representation is obtained by the maximum pool and space of Pyramid method, and then the linear support vector machine is used for pedestrian recognition, feature extraction of depth architecture. It improves effectively the accuracy of subsequent recognition. Experimental results show that the proposed method has a high recognition rate.
- Pedestrian recognition,
- Hybrid structure,
- Deep learning,
- Depth hierarchical coding,
- Restricted Boltzmann Machine (RBM)

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References(18)

References

DALAL N and TRIGGS B. Histograms of oriented gradients for human detection[C]. Proceedings of IEEE Computer Society Conference on in Computer Vision and Pattern Recognition. San Diego, 2005: 886-893. doi: 10.1109/CVPR. 2005.177.

ARMANFARD N, KOMEILI M, and KABIR E. TED: a texture-edge descriptor for pedestrian detection in video sequences[J]. Pattern Recognition, 2012, 45(3): 983-992. doi: 10.1016/j.patcog.2011.08.010.

YAN Zhiguo, YANG Fang, WANG Jian, et al. Face orientation detection in video stream based on Harr-like feature and LQV classifier for civil video surveillance[C]. IET International Conference on Smart and Sustainable City (ICSSC), Shanghai, 2013: 161-165. doi: 10.1049/cp.2013. 2029.

XIAO Pan, CAI Nian, TANG Bochao, et al. Efficient SIFT descriptor via color quantization[C]. IEEE International Conference on Consumer Electronics, Shenzhen, 2014: 1-3. doi: 10.1109/ICCE-China.2014.7029876.

YANG Jian, XU Wei, LIU Yu, et al. Real-time discrimination of frontal face using integral channel features and Adaboost[C]. IEEE Conference on Software Engineering and Service Science (ICSESS), Beijing, 2014: 360-363. doi: 10. 1109/ICSESS.2014.6933582.

WU Shuqiong and NAGAHASHI H. Parameterized AdaBoost: introducing a parameter to speed up the training of real AdaBoost[J]. IEEE Signal Processing Letters, 2014, 21(6): 687-691.doi: 10.1109/LSP.2014.2313570.

SCHMIDHUBER J. Deep learning in neural networks: an overview[J]. Neural Networks, 2015, 61: 85-117. doi: 10.1016/ j.neunet.2014.09.003.

RANZATO M, BOUREAU Y, and LECUN Y. Sparse feature learning for deep belief networks[C]. Proceedings of Annual Conference on Neural Information Processing Systems (NIPS), Vancouver, 2007: 1185-1192.

余凯, 贾磊, 陈雨强, 等. 深度学习的昨天、今天和明天[J]. 计算机研究与发展, 2013, 50(9): 1799-1804.

YU Kai, JIA Lei, CHEN Yuqiang, et al. Deep learning: yesterday, today, and tomorrow[J]. Journal of Computer Research and Development, 2013, 50(9): 1799-1804.

LAW M T, THOME N, and CORD M. Bag-of-Words Image Representation: Key Ideas and Further Insight[M]. Switzerland, Springer International Publishing, 2014: 29-52.

WU Chunpeng, FAN Wei, HE Yuan, et al. Handwritten character recognition by alternately trained relaxation convolutional neural network[C]. International Conference on Frontiers in Handwriting Recognition, Heraklion, 2014: 291-296. doi: 10.1109/ICFHR.2014.56.

SOHN K, JUNG D Y, LEE H, et al. Efficient learning of sparse, distributed, convolutional feature representations for object recognition[C]. 2011 IEEE International Conference on Computer Vision (ICCV), Barcelona, 2011: 2643-2650. doi: 10.1109/ICCV.2011.6126554.

LEE H, GROSSE R, RANGANATH R, et al. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations[C]. International Conference on Machine Learning, Montreal, 2009: 609-616. doi: 10.1145 /1553374.1553453.

BAI Y, YU W, XIAO T, et al. Bag-of-words based deep neural network for image retrieval[C]. Proceedings of the ACM International Conference on Multimedia, New York, 2014: 229-232. doi: 10.1145/2647868.2656402.

BOUREAU Y, BACH F, LECUN Y, et al. Learning mid-level features for recognition[C]. IEEE Conference on Computer Vision Pattern Recognition, 2010: 2559-2566. doi:10. 1109/CVPR.2010.5539963.

YU K, LIN Y, and LAFFERTY J. Learning image representations from the pixel level via hierarchical sparse coding[C]. IEEE Conference on Computer Vision Pattern Recognition, Colorado Springs, 2011: 1713-1720. doi: 10. 1109/CVPR.2011.5995732.

HINTON G E. Training products of experts by minimizing Ccontrastive divergence[J]. Neural Computation, 2002, 14(8): 1771-1800. doi: 10.1162/089976602760128018.

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