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http://dx.doi.org/10.6109/jkiice.2019.23.3.282

An OpenPose-based Child Abuse Decision System using Surveillance Video  

Yoo, Hye-Rim (Department of Electronics, Information and Communications Engineering, Daejeon University)
Lee, Bong-Hwan (Department of Electronics, Information and Communications Engineering, Daejeon University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.23, no.3, 2019 , pp. 282-290 More about this Journal
Abstract
Recently child abuse has occurred frequently in educational institutions such as daycare center and kindergarten. Therefore, government made it mandatory to install CCTVs, but it is not easy to inspect the CCTV images. In this paper, we propose a model for judging child abuse using CCTV images. First of all, child abuse is a physical abuse of children by adults, thus a model for classifying adults and children is needed. The existing Haar scheme uses the frontal image to classify adults and children. However, the OpenPose allows to classify adults and children regardless of frontal and side image. In this research, a child abuse judgment model was designed and implemented by applying characteristics of adult and child posture when a child was abused. Since the implemented system utilizes the currently installed CCTV image, it is possible to monitor the child abuse in real time without any additional installation, which enables us to cope with the abuse promptly.
Keywords
Machine Leaning; OpenPose; Decision Tree; Child Abuse; CCTV;
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  • Reference
1 Central Child Protection Agency, "Child Abuse & Near Korea," 2018.
2 B. W. Yoon, "A study on video based child and adult classification with biometry," M.S. thesis, Department of Electrical and Electronic Engineering, University of Kyungsung, Feb. 2015.
3 Z. Cao, T. Simon, S. E. Wei, and Y. Sheikh, "Realtime multi-Person 2D Pose Estimation using Part Affinity Fields," in Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7291-7299, 2016.
4 S. E. Wei, V. Ramakrishna, T. Kanade, and Y. Sheikh, "Convolutional Pose Machines," in Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4724-4732, 2016.
5 S. Qiao, Y. Wang, and J. Li, "Real-time human gesture grading based on OpenPose," in Proceeding of the 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, pp. 1-6, 2017.
6 K. S. Ahn, "Comparative Experiment and Evaluation on Machine Learning based k-Nearest Neighbor and Support Vector Machine," M.S. thesis, Department of Information and Communication Engineering, University of Dankook, Dec. 2016.
7 T. M. Cover, and P. E. Hart, "Nearest neighbor pattern classification," IEEE Transactions on Information Theory, vol. it-13, no. 1, pp. 21-27, 1967.
8 Y. Tang, "Deep learning using linear support vector machines," Workshop on Challenges in Representation Learning, ICML, 2013.
9 S. Tong, and D. Koller, "Support Vector Machine Active Learning with Applications to Text Classification," Journal of Machine Learning Research, vol.2(1), pp. 45-66, Nov. 2002.
10 C. C. Chang, and C. J. Lin, "LIBSVM: A library for support vector machines," Transaction on Intelligent Systems and Technology, vol. 2(3), no. 27, Nov. 2011.
11 A. Papagelis, and D. Kalles, "Breeding Decision Trees Using Evolutionary Techniques," in Proceeding of the Eighteenth International Conference on Machine Learning, pp. 93-400, 2001.
12 CMU-Perceptual-Computing-La. OpenPose [Internet]. Available: https://github.com/CMU-Perceptual-Computing-Lab/openpose.
13 A. C. Muller and S. Guido, Machine Learning with Python, 1st ed. Germany, O'Reilly Media Inc, 2016.
14 Image Set. [Internet]. Available: https://image-net.org/.