International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
권호 표지
DOI QR코드

DOI QR Code

Automatic Detection of Work Distraction with Deep Learning Technique for Remote Management of Telecommuting

  • Lee, Wan Yeon (Dept. of Computer Science, Dongduk Women's University)
  • Received : 2021.01.13
  • Accepted : 2021.01.21
  • Published : 2021.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose an automatic detection scheme of work distraction for remote management of telecommuting. The proposed scheme periodically captures two consequent computer screens and generates the difference image of these two captured images. The scheme applies the difference image to our deep learning model and makes a decision of abnormal patterns in the difference image. Our deep learning model is designed with the transfer learning technique of VGG16 deep learning. When the scheme detects an abnormal pattern in the difference image, it hides all texts in the difference images to protect disclosure of privacy-related information. Evaluation shows that the proposed scheme provides about 96% detection accuracy.

Keywords

References

  1. Alex Krizhevsky, Ilya Sutskever and Geoffery E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks", Advances in Neural Information Processing Systems (NIPS) (Dec. 2012), vol. 25, pp. 1097-1105. DOI: 10.1145/3065386
  2. Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun., "Deep Residual Learning for Image Recognition", arXiv:1512.03385, Dec. 2015.
  3. Takeshi Nakazawa and Deepak V. Kulkarni, "Anomaly Detection and Segmentation for Wafer Defect Patterns Using Deep Convolutional Encoder-Decoder Neural Network Architectures in Semiconductor Manufacturing", IEEE Transactions on Semiconductor Manufacturing, vol. 32, no. 2, pp. 250-256, May 2019. DOI: 10.1109/TSM.2019.2897690
  4. Romero Morais, Vuong Le, Truyen Tran, Budhaditya Saha, Moussa Mansour and Svetha Venkatesh, "Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11996-12004, Jun. 2019. DOI: 10.1109/CVPR.2019.01227
  5. Yi Zhu and Shawn Newsam, "Motion-Aware Feature for Improved Video Anomaly Detection", arXiv:1907.10211, Jul. 2019.
  6. Kuldeep M. Birada, Ayushi Gupta, Murari Mandal and Santosh K. Vipparthi, "Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos", arXiv:1906.04574, Jun. 2019.
  7. Sajja T. Krishna and Hemantha K. Kalluri, "Deep Learning and Transfer Learning Approaches for Image Classification", International Journal of Recent Technology and Engineering, vol. 7, no. 5S4, pp. 427-432, Feb. 2019. DOI: 10.1016/j.patrec.2017.10.037
  8. Karen Simonyan and Andrew Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition", arXiv:1409.1556, Sep. 2014.
  9. Francois Chollet, Deep Learning with Python, ISBN 9781617294433, Manning Publications, Nov. 2017.