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
- 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.
- Copyright
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
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 - Proceedings of the 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016) PB - Atlantis Press SP - 718 EP - 724 SN - 1951-6851 UR - https://doi.org/10.2991/aest-16.2016.96 DO - 10.2991/aest-16.2016.96 ID - Zhang2016/11 ER -