Proceedings of the 2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016)

Pedestrian Counting via Deep Convolutional Neural Networks In Crowded Scene

Authors
Jingwei Li, Jianxin Song
Corresponding Author
Jingwei Li
Available Online September 2016.
DOI
https://doi.org/10.2991/amitp-16.2016.67How to use a DOI?
Keywords
pedestrian, counting, crowd, CNN, EIN.
Abstract
Currently pedestrian counts mainly faces two major problems in the crowd scene: expression of pedestrian's features and perspective shade. To address this problem, we propose a deep convolutional neural network (CNN) and Ensemble Inference Network (EIN) for crowd pedestrian recognizing and counting. First, we propose a Perspective-Correct Interpolation model for extract more features in crowded scenes. Then, we design a convolution layer which contains preventing occlusion layer in the neural network. Finally to achieve lower error rates, our CNN-based method introduces Ensemble Inference Network to the training and classification processes. Experimental results show that the error rate of the proposed method is 0.28 and possess accurate recognition in pedestrian comparable performance to state-of-the-art methods in crowded scenes.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016)
Part of series
Advances in Computer Science Research
Publication Date
September 2016
ISBN
978-94-6252-245-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/amitp-16.2016.67How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Jingwei Li
AU  - Jianxin Song
PY  - 2016/09
DA  - 2016/09
TI  - Pedestrian Counting via Deep Convolutional Neural Networks In Crowded Scene
BT  - 2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/amitp-16.2016.67
DO  - https://doi.org/10.2991/amitp-16.2016.67
ID  - Li2016/09
ER  -