Proceedings of the 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)

Off-position detection based on convolutional neural network

Authors
Tianbing Zhang, Wang Luo, Qiwei Peng, Gongyi Hong, Min Feng, Yuan Xia, Lei Yu, Xu Wang, Yang Li
Corresponding Author
Tianbing Zhang
Available Online November 2016.
DOI
https://doi.org/10.2991/aest-16.2016.96How to use a DOI?
Keywords
off-position detection; CNN; classification.
Abstract
As a part of the intelligent video surveillance, off-position detection,which needs a real-time and precise algorithm, is used to detect whether the person on duty is absent from working position.This work is necessary for improving efficiency and reducing human resource consumption.Considering the excellent performance of convolutional neural network in image classification, we first propose a method for off-position detection using CNN in this paper and get good results.Furthermore,we introduce a new dataset for working position by generating crops from video frames.Then we randomly generate 224x224 crops from training images to fine-tune our deep neural network.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)
Part of series
Advances in Intelligent Systems Research
Publication Date
November 2016
ISBN
978-94-6252-257-2
ISSN
1951-6851
DOI
https://doi.org/10.2991/aest-16.2016.96How 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  - Tianbing Zhang
AU  - Wang Luo
AU  - Qiwei Peng
AU  - Gongyi Hong
AU  - Min Feng
AU  - Yuan Xia
AU  - Lei Yu
AU  - Xu Wang
AU  - Yang Li
PY  - 2016/11
DA  - 2016/11
TI  - Off-position detection based on convolutional neural network
BT  - 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/aest-16.2016.96
DO  - https://doi.org/10.2991/aest-16.2016.96
ID  - Zhang2016/11
ER  -