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Measurement of missing video frames in NPP control room monitoring system using Kalman filter

  • Mrityunjay Chaubey (Computer Science, Centre for Interdisciplinary Mathematical Sciences, Institute of Science, Banaras Hindu University) ;
  • Lalit Kumar Singh (Department of Computer Science & Engineering, IIT (BHU)) ;
  • Manjari Gupta (Computer Science, Centre for Interdisciplinary Mathematical Sciences, Institute of Science, Banaras Hindu University)
  • 투고 : 2022.06.24
  • 심사 : 2022.08.27
  • 발행 : 2023.01.25

초록

Using the Kalman filtering technique, we propose a novel method for estimating the missing video frames to monitor the activities inside the control room of a nuclear power plant (NPP). The purpose of this study is to reinforce the existing security and safety procedures in the control room of an NPP. The NPP control room serves as the nervous system of the plant, with instrumentation and control systems used to monitor and control critical plant parameters. Because the safety and security of the NPP control room are critical, it must be monitored closely by security cameras in order to assess and reduce the onset of any incidents and accidents that could adversely impact the safety of the NPP. However, for a variety of technical and administrative reasons, continuous monitoring may be interrupted. Because of the interruption, one or more frames of the video may be distorted or missing, making it difficult to identify the activity during this time period. This could endanger overall safety. The demonstrated Kalman filter model estimates the value of the missing frame pixel-by-pixel using information from the frame that occurred in the video sequence before it and the frame that will occur in the video sequence after it. The results of the experiment provide evidence of the effectiveness of the algorithm.

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참고문헌

  1. Pooja Singh, Lalit Kumar Singh, Modeling and measuring common cause failures in measurement of reliability of nuclear power plant systems, IEEE Transactions on Instrumentation and Measurement 70 (2021) 1-8.
  2. Pooja Singh, Lalit Kumar Singh, Improved measurement accuracy in critical parameters of safety-critical systems WithMultisensor data fusion, IEEE Transactions on Instrumentation and Measurement 70 (2021) 1-8.
  3. Chih-Wei Yang, Tsung-Ling Hsieh, Shiau-Feng Lin, Chiuhsiang Joe Lin, Hui-Ming Teng, Yu-Fang Chiu, Operators' signal-detection performance in video display unit monitoring tasks of the main control room, Safety science 49 (10) (2011) 1309-1313. https://doi.org/10.1016/j.ssci.2011.04.010
  4. Mrityunjay Chaubey, Lalit Kumar Singh, Manjari Gupta, A review of missing video frame estimation techniques for their suitability analysis in NPP, Nuclear Engineering and Technology 54 (April) (2022) 1153-1160. https://doi.org/10.1016/j.net.2021.10.012
  5. Jorge, F. Carlos Alexandre, Antonio Carlos A. Mol, Jose M. Seixas, Eduardo Antonio B. Silva, Raphael E. Cota, Bruno L. Ramos, People Detection in Nuclear Plants by Video Processing for Safety Purpose, 2011.
  6. Xun Chen, Juan Cheng, Rencheng Song, Yu Liu, Rabab Ward, Z. Jane Wang, Video-based heart rate measurement: recent advances and future prospects, IEEE Transactions on Instrumentation and Measurement 68 (10) (2018) 3600-3615. https://doi.org/10.1109/TIM.2018.2879706
  7. Luisa HB. Liboni, Mauricio C. de Oliveira, Ivan N. da Silva, Optimal kalman estimation of symmetrical sequence components, IEEE Transactions on Instrumentation and Measurement 69 (11) (2020) 8844-8852. https://doi.org/10.1109/TIM.2020.2995231
  8. Du-ming Tsai, Yi-chun Hsieh, Machine vision-based positioning and inspection using expectation-maximization technique, IEEE Transactions on Instrumentation and Measurement 66 (11) (2017) 2858-2868. https://doi.org/10.1109/TIM.2017.2717284
  9. Bo Yan, Hamid Gharavi, A hybrid frame concealment algorithm for H. 264/AVC, IEEE Transactions on Image Processing 19 (1) (2009) 98-107. https://doi.org/10.1109/TIP.2009.2032311
  10. Ryan Szeto, Ximeng Sun, Kunyi Lu, Jason J. Corso, A temporally-aware interpolation network for video frame inpainting, IEEE Transactions on Pattern Analysis and Machine Intelligence 42 (5) (2019) 1053-1068. https://doi.org/10.1109/TPAMI.2019.2951667
  11. Ci Wang, Lei Zhang, Yuwen He, Yap-Peng Tan, Frame rate up-conversion using trilateral filtering, IEEE Transactions on Circuits and Systems for Video Technology 20 (6) (2010) 886-893. https://doi.org/10.1109/TCSVT.2010.2046057
  12. Ting-Lan Lin, Hua-Wei Tseng, Yangming Wen, Fu-Wei Lai, Ching-Hsuan Lin, Chuan-Jia Wang, Reconstruction algorithm for lost frame of multiview videos in wireless multimedia sensor network based on deep learning multilayer perceptron regression, IEEE Sensors Journal 18 (23) (2018) 9792-9801. https://doi.org/10.1109/JSEN.2018.2865916
  13. Liu Hongbin, Ruiqin Xiong, Debin Zhao, Siwei Ma, Wen Gao, Multiple hypotheses Bayesian frame rate up-conversion by adaptive fusion of motion-compensated interpolations, IEEE transactions on circuits and systems for video technology 22 (8) (2012) 1188-1198. https://doi.org/10.1109/TCSVT.2012.2197081
  14. Wang Shen, Wenbo Bao, Guangtao Zhai, Li Chen, Xiongkuo Min, Zhiyong Gao, Video frame interpolation and enhancement via pyramid recurrent framework, IEEE Transactions on Image Processing 30 (2020) 277-292. https://doi.org/10.1109/TIP.2020.3033617
  15. Xiaozhang Liu, Hui Liu, Yuxiu Lin, Video frame interpolation via optical flow estimation with image inpainting, International Journal of Intelligent Systems 35 (12) (2020) 2087-2102. https://doi.org/10.1002/int.22285
  16. Shihua Cui, Huijuan Cui, Kun Tang, An effective error concealment scheme for heavily corrupted H. 264/AVC videos based on Kalman filtering, Signal, Image and Video Processing 8 (8) (2014) 1533-1542. https://doi.org/10.1007/s11760-012-0390-5
  17. Avinash Paliwal, NimaKhademi Kalantari, Deep slow motion video reconstruction with hybrid imaging system, IEEE Transactions on Pattern Analysis and Machine Intelligence 42 (7) (2020) 1557-1569. https://doi.org/10.1109/TPAMI.2020.2987316
  18. Xingang Liu, Laurence T. Yang, Wei Zhu, Kwanghoon Sohn, Efficient temporal error concealment algorithm for H. 264/AVC inter frame decoding, International Journal of Communication Systems 24 (10) (2011) 1282-1297. https://doi.org/10.1002/dac.1193
  19. Wenbo Bao, Wei-Sheng Lai, Xiaoyun Zhang, Zhiyong Gao, Ming-Hsuan Yang, Memc-net: motion estimation and motion compensation driven neural network for video interpolation and enhancement, IEEE transactions on pattern analysis and machine intelligence 43 (3) (2019) 933-948.
  20. Yasuyuki Matsushita, Weina Ge EyalOfek, Xiaoou Tang, Heung-Yeung Shum, Full-frame video stabilization with motion inpainting, IEEE Transactions on pattern analysis and Machine Intelligence 28 (7) (2006) 1150-1163. https://doi.org/10.1109/TPAMI.2006.141
  21. Yonatan Wexler, Eli Shechtman, Michal Irani, Space-time completion of video, IEEE Transactions on pattern analysis and machine intelligence 29 (3) (2007) 463-476. https://doi.org/10.1109/TPAMI.2007.60
  22. Michael Rucci, Russell C. Hardie, Kenneth J. Barnard, Computationally efficient video restoration for Nyquist sampled imaging sensors combining an affine-motion-based temporal Kalman filter and adaptive Wiener filter, Applied optics 53 (13) (2014) C1-C13. https://doi.org/10.1364/AO.53.0000C1
  23. Jesse Scott, Michael A. Pusateri, Duane Cornish, Kalman Filter Based Video Background Estimation, 2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009), IEEE, 2009.
  24. Shigeki Takahashi, Takahiro Ogawa, Haseyama Miki, Restoration Method of Missing Areas in Video Images Using Kalman Filter, 2007. International Conference on Kansei Engineering and Emotion Research.
  25. Alessandro Ferrero, Harsha VardhanaJetti, Simona Salicone, The possibilistic kalman filter: definition and comparison with the available methods, IEEE Transactions on Instrumentation and Measurement 70 (2020) 1-11.
  26. Gary Bishop, Greg Welch, Proc of SIGGRAPH, Course, An Introduction to the Kalman Filter, vol. 8, 2001, p. 41, 27599-23175.