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)

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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  • 1. Iván Gómez Conde
  • 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.  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.  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.  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.  Walking  Exaggerated Walking  Jogging  Bending over  Lying Down  Falling
  • 7. Exagerated Walking
  • 8. Bending over
  • 9. Falling
  • 10.  394 video sequences, 6 human actions, 12 different human subjects
  • 11.  C++  OpenCV (Open Source Computer Vision)  Python  FFmpeg  Octave  Ubuntu
  • 12. Training Testing
  • 13.  M1: Silhouttes (foreground object)
  • 14.  M2: Motion History Instance (MHI)
  • 15.  M3: Motion Vector Flow Instances (MVFI)
  • 16.  Video sequences with 3 types of motion templates: m1, m2, m3
  • 17. https://www.youtube.com/watch?v=sWY_mQ2_Gco
  • 18.  Training set: where is the image pertaining to the ith class and having the jth frame within the sequence.
  • 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. This method projects the original images of the sequences onto the new multidimensional space:
  • 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.  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. The points of PCA space are projected onto the new multidimensional space:
  • 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.  Two methods are used for determining the class of an unknown test sequence:  KNN clasifier  SVM clasifier
  • 26.  Summary of some representative training statistics, showing the average number of images, the average training times, and total run times.
  • 27.  Normalized histograms of recognition rates obtained from the three different spatio-temporal motion templates from
  • 28.  Comparison of recognition rates for different multiclass training: number of actions as a function of different number of persons included in training.
  • 29.  Comparison of recognition rates for different motion templates as a function of incrementally including more people in the training set.
  • 30.  Comparison of motion templates for binary classification of actions.
  • 31.  The results of PCA and LDA training space with six different action classes of this study.
  • 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.  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. http://www.youtube.com/watch?v=bNBMRNjIu_g&feature=player_embedded