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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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(6): 1528-1535

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 cstr: 32379.14.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

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