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Intelligent video monitoring


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Intelligent video monitoring for anomalous event detection.

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Intelligent video monitoring

  1. 1. I. Gómez-Conde, D. Olivieri, X.A. Vila Sobrino, A. Orosa-Rodríguez (University of Vigo) Salamanca (6-8th April, 2011) Intelligent Video Monitoring for Anomalous Event Detection
  2. 2. • Introduction • Our approach oSoftware algorithms for the tele-assistance for the elderly oMultiple object tracking techniques oBehavior detectors based on human body positions • Experimental Results • Conclusions Index Iván Gómez Conde
  3. 3. o % people (65 years and over) o % youth (under 15 years) o In 2050, % elderly people % youth o Problems:  Sociologic  Economic Computer Vision can be used as early warning monitor for anomalous event detection!!!  The aging of the population has increased dramatically. Current problem Iván Gómez Conde
  4. 4.  The motivation for this paper is the development of a tele-assistance application. Detect foreground objects Track these objects in time Action Recognition Motivation Iván Gómez Conde
  5. 5. o Image analysis o Machine learning o Transate the low level signal to a higher semantic level o Inference actions and behaviors  Present computer aplications go far beyond the simple security camera of a decade ago and now include: What is the monitoring? Iván Gómez Conde
  6. 6. Method for comparing foreground- background segmentation Feature vector tracking algorithm Simple real-time histogram based algorithm for discriminating movements and actions  There are several original contributions proposed by this paper: Contributions Iván Gómez Conde
  7. 7. • C++ • OpenCV (Open Source Computer Vision)  Qt  Octave Software Iván Gómez Conde
  8. 8. System Iván Gómez Conde  This software is an experimental application. The graphical interface provides maximum information.
  9. 9. Detecting movement  There are several background subtraction methods. We use two methods: • Running Average • Gaussian Mixture Model Iván Gómez Conde
  10. 10. Running Average A = Matrix of accumulated pixels I = Image Nf = nº of used frames α = weighting parameter Є [0,1]  Each point of the background is calculated with the mean of the backgrounds over Nf previous frames. At(Nf) = (1-α) At-1(Nf) + α It Iván Gómez Conde
  11. 11. Running average Iván Gómez Conde
  12. 12. Gaussian Mixture Model  This method models each background pixel as a mixture of K Gaussian distributions o K is tipically from 3 to 5 o Eliminates many of the artefacts that Running Average is unable to treat Iván Gómez Conde
  13. 13. Gaussian Mixture Model Iván Gómez Conde
  14. 14. Testing Methods (% error) FN + FP 640∙480 Iván Gómez Conde • False Negatives (FN): Foreground pixels labeled as background • False Positives (FP): Background pixels labeled as foreground % error =
  15. 15. Finding individual objects • Foreground objects rectangular “blobs” detect blob while (∃ blob) do apply mask create color histogram aproximate with gauss create feature vector detect new blob end while Iván Gómez Conde
  16. 16. Feature vector for classification Feature Vector Size and coordinates of the blob center Gaussian fitted values of RGB components Motion vector Iván Gómez Conde
  17. 17. Discrimination objects Norm difference of red channel Normdifferenceofgreenchannel Iván Gómez Conde
  18. 18. Tracking algorithm  Once objects have been separated and characterized by their feature vector, we tracks  Tracking is performed by matching features of the rectangular regions Iván Gómez Conde
  19. 19. Tracking algorithm • Position from t to t+1 (x = xo + vt) Iván Gómez Conde
  20. 20. Time chart Bg-Fg Seg. Blob Detection Normal Video Video with Qt Frame 1 28.3 ms 168.5 ms 33.2 ms 2.5 ms Frame 30 847.5 ms 5065.4 ms 997.2 ms 75.82 ms Frame 361 10198.2 ms 60954.1 ms 12000 ms 912.36 ms Iván Gómez Conde
  21. 21. Detecting gestures  We have considered a limited domain of events  Discrimination arms gestures o The mass histogram o Statistical moments Iván Gómez Conde
  22. 22. Detecting actions Normalized Histogram Iván Gómez Conde • Basic body position o Upright o Lying down • The inset image is the histogram normalized to unity
  23. 23. Discrimination actions Figure 1 Figure 2 Figure 3 µ 0.54 0.33 0.44 σ 0.21 0.17 0.21 µ3 0.17 3.99 3.12 Iván Gómez Conde
  24. 24. Conclusions  Our software aplication will allow track people and discriminate basic actions  The system is actually part of a more complete tele- monitoring system  The paper opens many possibilities for future study. o Using our quantitative comparison to optimize parameters o Combining feature vector with sequential Monte Carlo methods Iván Gómez Conde
  25. 25. Conclusions  The histogram model developed in this paper provides detection for a limited set of actions and events: Real-time method Easy to implement Should have utility in real systems It is not sufficiently robust Iván Gómez Conde
  26. 26. Many thanks for your attention Iván Gómez Conde