Iván Gómez Conde
1. Introduction
2. Related Work
3. Theory and Algorithms
 MILE Dataset
 Motion Vector Flow Instances (MVFI)
 Mathematic...
 Automatically determining human actions and
gestures from videos or real-time cameras.
 New spation-temporal representa...
 Automatically determining human actions and
gestures from videos or real-time cameras.
 New spation-temporal representa...
 Database of video scenes.
 6 human actions
 5 video sequences for each action
 12 different human subjects
 Sampling...
 Walking
 Exaggerated Walking
 Jogging
 Bending over
 Lying Down
 Falling
Exagerated
Walking
Bending
over
Falling
 394 video sequences, 6 human actions, 12
different human subjects
 C++
 OpenCV (Open Source
Computer Vision)
 Python
 FFmpeg
 Octave
 Ubuntu
Training Testing
 M1: Silhouttes (foreground object)
 M2: Motion History Instance (MHI)
 M3: Motion Vector Flow Instances (MVFI)
 Video sequences with 3 types of motion
templates: m1, m2, m3
https://www.youtube.com/watch?v=sWY_mQ2_Gco
 Training set:
where is the image pertaining to the ith class and having
the jth frame within the sequence.
 The PCA canonical space is constructed from the
orthogonal vectors that possess the most variance
between all the pixels...
This method projects the original images of the
sequences onto the new multidimensional space:
 We apply a Fisher criteria that maximizes the
between class variance and minimizes the within
class variance
 The Fishe...
 We can write the corresponding eigenvalue
equation:
 The new orthogonal basis that takes the points
of the PCA space an...
The points of PCA space are projected onto the new
multidimensional space:
 In this paper we performed an N-fold cross-
validation training process, where we constructed
all posible binary and mul...
 Two methods are used for determining the class of
an unknown test sequence:
 KNN clasifier
 SVM clasifier
 Summary of some representative training
statistics, showing the average number of
images, the average training times, an...
 Normalized histograms of recognition rates
obtained from the three different spatio-temporal
motion templates from
 Comparison of recognition rates for different
multiclass training: number of actions as a function
of different number o...
 Comparison of recognition rates for different
motion templates as a function of incrementally
including more people in t...
 Comparison of motion templates for binary
classification of actions.
 The results of PCA and LDA training space with six
different action classes of this study.
 We performed a 10-fold cross validation for all six
action classes and all the training sequences of our
database.
 The...
 This paper has compared two different motion
templates with a new spatio-temporal motion
template, MVFI.
 MVFI outperfo...
http://www.youtube.com/watch?v=bNBMRNjIu_g&feature=player_embedded
MVFI Meeting (January 14th, 2011)
MVFI Meeting (January 14th, 2011)
MVFI Meeting (January 14th, 2011)
MVFI Meeting (January 14th, 2011)
MVFI Meeting (January 14th, 2011)
MVFI Meeting (January 14th, 2011)
MVFI Meeting (January 14th, 2011)
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MVFI Meeting (January 14th, 2011)

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Eigenspace-based Fall detection and activity recognition from motion templates and machine learning. We present a new spatio-temporal template: Motion Vector Flow Instance (MVFI)

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MVFI Meeting (January 14th, 2011)

  1. 1. Iván Gómez Conde
  2. 2. 1. Introduction 2. Related Work 3. Theory and Algorithms  MILE Dataset  Motion Vector Flow Instances (MVFI)  Mathematics 4. Results 5. Conclusions 6. Further Research
  3. 3.  Automatically determining human actions and gestures from videos or real-time cameras.  New spation-temporal representation: Motion Vector Flow Instance (MVFI)  We compare it with other 2 motion templates: silhoutte and Motion History Instance (MHI)  Representations with a canonical transformation with PCA and LDA.
  4. 4.  Automatically determining human actions and gestures from videos or real-time cameras.  New spation-temporal representation: Motion Vector Flow Instance (MVFI)  We compare it with other 2 motion templates: silhoutte and Motion History Template (MHI)  Representations with a canonical transformation with PCA and LDA.
  5. 5.  Database of video scenes.  6 human actions  5 video sequences for each action  12 different human subjects  Sampling rate (25 frames/second)  Static camera  Videos were saved in AVI MPEG
  6. 6.  Walking  Exaggerated Walking  Jogging  Bending over  Lying Down  Falling
  7. 7. Exagerated Walking
  8. 8. Bending over
  9. 9. Falling
  10. 10.  394 video sequences, 6 human actions, 12 different human subjects
  11. 11.  C++  OpenCV (Open Source Computer Vision)  Python  FFmpeg  Octave  Ubuntu
  12. 12. Training Testing
  13. 13.  M1: Silhouttes (foreground object)
  14. 14.  M2: Motion History Instance (MHI)
  15. 15.  M3: Motion Vector Flow Instances (MVFI)
  16. 16.  Video sequences with 3 types of motion templates: m1, m2, m3
  17. 17. https://www.youtube.com/watch?v=sWY_mQ2_Gco
  18. 18.  Training set: where is the image pertaining to the ith class and having the jth frame within the sequence.
  19. 19.  The PCA canonical space is constructed from the orthogonal vectors that possess the most variance between all the pixels from the image sequence.  Thus, with , we have:
  20. 20. This method projects the original images of the sequences onto the new multidimensional space:
  21. 21.  We apply a Fisher criteria that maximizes the between class variance and minimizes the within class variance  The Fisher linear discriminant function, , if given by the ratio:
  22. 22.  We can write the corresponding eigenvalue equation:  The new orthogonal basis that takes the points of the PCA space and transforms them to this new space, we call the LDA space, through:
  23. 23. The points of PCA space are projected onto the new multidimensional space:
  24. 24.  In this paper we performed an N-fold cross- validation training process, where we constructed all posible binary and multiclass combinations in our dataset. Training: Testing:
  25. 25.  Two methods are used for determining the class of an unknown test sequence:  KNN clasifier  SVM clasifier
  26. 26.  Summary of some representative training statistics, showing the average number of images, the average training times, and total run times.
  27. 27.  Normalized histograms of recognition rates obtained from the three different spatio-temporal motion templates from
  28. 28.  Comparison of recognition rates for different multiclass training: number of actions as a function of different number of persons included in training.
  29. 29.  Comparison of recognition rates for different motion templates as a function of incrementally including more people in the training set.
  30. 30.  Comparison of motion templates for binary classification of actions.
  31. 31.  The results of PCA and LDA training space with six different action classes of this study.
  32. 32.  We performed a 10-fold cross validation for all six action classes and all the training sequences of our database.  The Motion Vector Flow Instance outperforms all other motion templates.
  33. 33.  This paper has compared two different motion templates with a new spatio-temporal motion template, MVFI.  MVFI outperforms other methods for detecting actions characterized by large velocities.  This work suggest that it is important to preserve velocity information in each image sequence.  MVFI works well in all situations for action recognition: different people, different clothing types…  Future studies shall consider both different camer angles and distances.
  34. 34. http://www.youtube.com/watch?v=bNBMRNjIu_g&feature=player_embedded

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