DOI: https://doi.org/10.14710/jtsiskom.2020.13660

Penghitung orang pada video CCTV menggunakan metode histogram of oriented gradient dan filter Kalman

People counter on CCTV video using histogram of oriented gradient and Kalman filter methods

1Master of Computer Science, Universitas Gadjah Mada, Indonesia

2Department of Computer Science and Electronic, Universitas Gadjah Mada, Indonesia

Received: 7 Feb 2020; Revised: 12 May 2020; Accepted: 26 May 2020; Available online: 8 Jun 2020; Published: 31 Jul 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer under http://creativecommons.org/licenses/by-sa/4.0.

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Abstract
CCTV cameras have an important function in the field of public service, especially for convenience. The objects recorded through CCTV cameras are processed into information to support service satisfaction in the community. This study uses the function of CCTV for people counting from objects recorded by a camera. Currently, the process of detecting and tracking people takes a long time to detect all frames. In this study, the frame selection into keyframes uses the mutual information entropy method. The keyframes processing uses the Histogram of Oriented Gradient (HOG) and Kalman filter methods. The proposed method results F1 value of 0.85, recall of 76 %, and precision of 97 % with winStride parameter (12,12), scale 1.05, and the distance of the human object to CCTV 4 meters.
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Keywords: HOG; Kalman filter; mutual information entropy; people counting; people detection
Funding: Lembaga Pengelola Dana Pendidikan (LPDP), Indonesia;Universitas Gadjah Mada, Indonesia

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Section: Original Research Articles
Language : ID
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  1. D. Roqueiro and V. A. Petrushin, “Counting people using video cameras,” International Journal of Parallel, Emergent and Distributed Systems, vol. 22, no. 3, pp. 193–209, Jun. 2007
  2. F. Sayadi, Y. Said, M. Atri, and R. Tourki, “Real time human detection in video streams,” p. 5, 2012
  3. Y. Chen, S. Guo, B. Zhang, and K. L. Du, “A Pedestrian Detection and Tracking System Based on Video Processing Technology,” in 2013 Fourth Global Congress on Intelligent Systems, Hong Kong, China, 2013, pp. 69–73
  4. H. L. Kidane, “Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence,” pp. 1–7, 2018
  5. F. Li and D. L. Li, “Pedestrian detection based on histogram of oriented gradient in intelligent transportation system,” Proceedings - 2013 6th International Conference on Intelligent Networks and Intelligent Systems, ICINIS 2013, pp. 78–81, 2013
  6. C. Li, L. Guo, and Y. Hu, “A New Method Combining HOG and Kalman Filter for Video-based Human Detection and Tracking,” International Congress on Image and Signal Processing, pp. 290–293, 2010
  7. J. Liu, J. Liu, and M. Zhang, “A detection and tracking based method for real-time people counting,” in 2013 Chinese Automation Congress, Changsha, Hunan, China, 2013, pp. 470–473
  8. X. Wang, C. Wang, and J. Yao, “A Heuristic Information Based System for People Counting,” in 2011 Third International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China, 2011, pp. 22–26
  9. C.-C. Chen, H.-H. Lin, and O. T.-C. Chen, “Tracking and counting people in visual surveillance systems,” in 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, 2011, pp. 1425–1428
  10. F. Zhu, X. Yang, J. Gu, and R. Yang, “A New Method for People-Counting Based on Support Vector Machine,” in 2009 Second International Conference on Intelligent Networks and Intelligent Systems, Tianjian, China, 2009, pp. 342–345
  11. S. kaur Riya, “REVIEW OF PURPOSED METHOD FOR KEY FRAME EXTRACTION FROM VIDEOS,” International Journal of Advanced Research in Computer Science, vol. 8, no. 7, pp. 377–379, Aug. 2017
  12. S. Z. Ouyang, L. Zhong, and R. Q. Luo, “The comparison and analysis of extracting video key frame,” IOP Conference Series: Materials Science and Engineering, vol. 359, no. 1, 2018
  13. D. M. Gavrila and S. Munder, “Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle,” in International Journal of Computer Vision, 2007, vol. 73, pp. 41–59
  14. Lina Sun and Yihua Zhou, “A key frame extraction method based on mutual information and image entropy,” in 2011 International Conference on Multimedia Technology, Hangzhou, China, 2011, pp. 35–38
  15. N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 886–893, 2005
  16. C. Tomasi, “Histograms of Oriented Gradients.” Comput. Vis. Sampler, 2012
  17. T. Fletcher, “Support Vector Machines Explained.” University College London, 2009
  18. M. Pérez-Ortiz, S. Jiménez-Fernández, P. Gutiérrez, E. Alexandre, C. Hervás-Martínez, and S. Salcedo-Sanz, “A Review of Classification Problems and Algorithms in Renewable Energy Applications,” Energies, vol. 9, no. 8, p. 607, Aug. 2016
  19. P. Chong and Y. H. Tay, “A Novel Pedestrian Detection and Tracking with Boosted HOG Classifiers and Kalman Filter,” 2016 IEEE Student Conference on Research and Development (SCOReD), pp. 1–5

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