Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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A Kidnapping Detection Using Human Pose Estimation in Intelligent Video Surveillance Systems

  • Park, Ju Hyun (Dept. of Information and Communication Engineering, Inha University) ;
  • Song, KwangHo (Dept. of Information and Communication Engineering, Inha University) ;
  • Kim, Yoo-Sung (Dept. of Information and Communication Engineering, Inha University)
  • Received : 2018.07.03
  • Accepted : 2018.07.25
  • Published : 2018.08.31
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Abstract

In this paper, a kidnapping detection scheme in which human pose estimation is used to classify accurately between kidnapping cases and normal ones is proposed. To estimate human poses from input video, human's 10 joint information is extracted by OpenPose library. In addition to the features which are used in the previous study to represent the size change rates and the regularities of human activities, the human pose estimation features which are computed from the location of detected human's joints are used as the features to distinguish kidnapping situations from the normal accompanying ones. A frame-based kidnapping detection scheme is generated according to the selection of J48 decision tree model from the comparison of several representative classification models. When a video has more frames of kidnapping situation than the threshold ratio after two people meet in the video, the proposed scheme detects and notifies the occurrence of kidnapping event. To check the feasibility of the proposed scheme, the detection accuracy of our newly proposed scheme is compared with that of the previous scheme. According to the experiment results, the proposed scheme could detect kidnapping situations more 4.73% correctly than the previous scheme.

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References

  1. Statistics Korea, http://stat.kosis.kr/statHtml_host/statHt ml.do?orgId=132&tblId=DT_13204_2011_211&conn_pa th=I2&dbUser=NSI_IN_132
  2. Choong-Sik. Chung, "A Case Study on the Operation Enhancement of Integrated CCTV Control Center at Busan Metropolitan City," Journal of Korean Association for Regional Information Society, Vol 18, No. 3, pp. 123-154, September 2015.
  3. YTN News: Detect immediately dangerous situations ... prevent crime with intelligent cctv, http://www.ytn.co.kr/ _ln/0115_201212241855130011
  4. Ryu-Hyeok. Gwon, et al, "A Kidnapping Detection Scheme Using Frame-Based Classification for Intelligent Video Surveillance," Proc. Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, pp. 345-354, October 2013.
  5. Ji-Hyen. Choi, et al, "A Prediction Method for Abnormal Behavior based on Omni-view Pattern," Proc. of the 42th KIISE Winter Conference, pp. 401-403, Korea, 2015.
  6. Soumi. Paul, et al, "Microsoft Kinect in Gesture Recognition: A Short Review," International Journal of Control Theory and Applications, Vol. 8, No. 5. pp. 2071-2076, 2015.
  7. Shih-En. Wei, et al, "Convolutional pose machines," IEEE Conference on Computer Vision and Pattern Recognition, pp. 4724-4732, 2016.
  8. Zhe. Cao, et al, "Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields" IEEE Conference on Computer Vision and Pattern Recognition, pp. 7291-7299, 2017.
  9. OpenPose library, https://github.com/CMU-PerceptualComputing-Lab/openpose
  10. Divya. J, "Automatic Video Based Surveillance System for Abnormal Behavior Detection," International Journal of Science and Research, Vol 4. No 7, pp 1743-1747, July 2013.
  11. Hall. Mark A, et al, "The WEKA data mining software: An Update ACM SIGKDD explorations newsletter," Vol. 11, No. 1, pp. 10-18, June. 2009. https://doi.org/10.1145/1656274.1656278
  12. Hall. Mark A, "Correlation-based feature selection for machine learning," Ph. D. Thesis, University of Waikato, pp. 1-198, Apr. 1999.
  13. Lei Xu, et al, "Best first strategy for feature selection," 9th International Conference on Pattern Recognition, pp. 706-708, Italy, 1988.
  14. Kevin P. Murphy, "Naive Bayes classifiers," University of British Columbia, Vol. 18, Oct. 2006.
  15. Neeraj. Bhargava, et al, "Decision Tree Analysis on J48 Algorithm for Data Mining," Proceedings of International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 3. No. 6, pp. 1114-1119, 2013.
  16. Marti A. Hearst, et. al, "Support vector machines," IEEE Intelligent Systems and their applications, Vol. 13, Issue. 4, pp. 18-28, 1998. https://doi.org/10.1109/5254.708428
  17. Powers, et. al, "Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation," Journal of Machine Learning Technologies, Vol. 2, No. 1, pp. 37-63, February 2011.

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