A computer vision-based system towards safety for nuclear plants personnel
Palavras-chave:
detection, safety, videoResumo
This work describes improvements in a surveillance system for safety purposes in nuclear plants. The objective
is to track people online in video, in order to estimate the dose received by personnel, during working tasks
executed in nuclear plants. The estimation will be based on their tracked positions and on dose rate mapping in
a nuclear research reactor, Argonauta. Cameras have been installed within Argonauta’s room, supplying the data
needed. Video processing methods were combined for detecting and tracking people in video. More specifically,
segmentation, performed by background subtraction, was combined with a tracking method based on color
distribution. The use of both methods improved the overall results. An alternative approach was also evaluated,
by means of blind source signal separation. Results are commented, along with perspectives.
Referências
E. Maggio, A. Cavallaro, Video Tracking: Theory and Practice, John Wiley & Sons, Ltd., Chichester, United Kingdon (2011).
International Commission on Radiological Protection, “ICRP Publication 103 – 2007 Recommendations of the International Commission on Radiological Protection,” Annals of the ICRP, Volume 37, pp.1-332 (2007).
C. A. F. Jorge, J. M. Seixas, E. A. B. Silva, A. C. A. Mól, R. E. Cota, B. L. Ramos, “People Detection in Nuclear Plants by Video Processing for Safety Purpose, Proceedings of the 2011 International Nuclear Atlantic Conference (INAC 2011), Belo Horizonte, MG, Brazil, Oct. 24-28 (2011).
C. Stauffer, W. E. L. Grimson, “Adaptive Background Mixture Models for Real-time Tracking,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Volume 2, pp.246-252 (1999).
C. Stauffer, W. E. L. Grimson, “Learning Patterns of Activity Using Real-time Tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 22, pp.747- 757 (2000).
G. R. Bradski, “Real Time Face and Object Tracking as a Component of a Perceptual User Interface,” Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV), Princeton, New Jersey, USA, pp.214-219, Oct. 19-21 (1998).
D. Comaniciu, V. Ramesh, P. Meer, “Kernel-based Object Tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 25, pp.564-577 (2003).
A. Hyvärinen, J. Karhunen, E. Oja, Independent Component Analysis, John Wiley & Sons, New York, USA (2001).
S. C. Augusto, A. C. A. Mól, C. A. F. Jorge, P. M. Couto, G. G. Cunha, L. Landau, “Use of Virtual Reality to Estimate Radiation Dose Rates in Nuclear Plants,” Proceedings of the 2007 International Nuclear Atlantic Conference (INAC 2007), Santos, SP, Brazil, Sept. 30–Oct. 5 (2007).
A. C. A. Mól, C. M. N. A. Pereira, V. G. G. Freitas, C. A. F. Jorge, “Radiation Dose Rate Map Interpolation in Nuclear Plants Using Neural Networks and Virtual Reality Techniques,” Annals of Nuclear Energy, Volume 38, pp.705-712 (2011).
G. Bradski, A. Kaehler, Learning OpenCV, O’Reilly Media, Inc., Sebastopol, USA (2008).
R. Laganière, OpenCV 2 Computer Vision Application Programming Cookbook, Packt Publishing Ltd., Birmingham, Mubai (2011).
P. KaewTraKulPong, R. Bowden, “An Improved Adaptive Background Mixture Model for Real Time Tracking with Shadow Detection,” Proceedings of the 2nd European Workshop on Advanced Video-Based Surveillance Systems (AVBS), London, UK, Sept. (2001).
Z. Zivkovic, “Improved Adaptive Gaussian Mixture Model for Background Subtraction,” Proceedings of the 17th International Conference on Pattern Recognition (ICPR), Cambridge, UK, Vol. 2, pp.28-31, Aug. 23-26 (2004).
D.-M. Tsai, S.-C. Lai, “Independent Component Analysis-based Background Subtraction for Indoor Surveillance,” IEEE Transactions on Image Processing, Volume 18, pp.158- 167 (2009).
P. Dollár, S. Belongie, P. Perona, “The Fastest Pedestrian Detector in the West,” Brittish Machine Vision Conference (BMVC), Aberystwith, UK, pp.68-1–68-11, Aug. 31–Sept. 03 (2010).
A. Adam, E. Rivlin, I. Shimshoni, “Robust Fragments-based Tracking Using the Integral Histogram,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), New York, NY, USA, Vol. 1, pp.798-805, June 17-22 (2006). INAC 2013, Recife, PE, Brazil.
C. R. Jung, A. Said, “Object Tracking Using Multiple Fragments,” Proceedings of the IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, pp.889-892, Nov. 7-12 (2009).
J. Bins, L. Dihl, C. R. Jung, “Target Tracking Using Multiple Patches and Weighted Vector Median Filters,” Journal of Mathematical Imaging and Vision, Volume 45, pp.293-307 (2013).
Y. Li, Q. Xie, G. Yu, “Real-time Tracking System Combining Mixture of Gaussian Model and Camshift Algorithm,” SPIE Proceedings, Volume 7820 (2010).
R. C. Gonzalez, R. E. Woods, Digital Image Processing, Prentice-Hall, Inc., Upper Saddle River, USA (2006).
D. D. Lee, H. S. Seung, “Learning the Parts of Objects by Non-negative Matrix Factorization”, Nature, Volume 401, pp.788-791 (1999).
