Human Action Recognition Based on Spacio-temporal features
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Human Action Recognition Based on Spacio-temporal features

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  • 1. Human Action Recognition Based on Spatio-temporal Features Nikhil Sawant and Dr. K.K. Biswas Dept. of CSE, Indian Institute of Technology, Delhi Third International Conference on Pattern Recognition and Machine Intelligence (PReMi’09)
  • 2. Human activity recognition Higher resolution Longer Time Scale Courtesy : Y. Ke, Fathi and Mori, Bobick and Davis, Schuldt et al, Leibe et al, Vaswani et al. Pose Estimation Event Detection Action Classification Tracking Activity Recognition
  • 3. Use Action recognition?
    • Video surveillance
    • Interactive environment
    • Video classification & indexing
    • Movie search
    • Assisted Care
    • Sports annotation
  • 4. Broad outline of our technique Action Class 1 Action Class 2 Action Class 3 Action Class n Motion analysis using Lucas –kanade technique Shape analysis using Viola-Jones feaures Combining motion and shape features over finite time interval …………… ...... Video with human actions Motions features Shape features Spatio-temporal features Learning features though AdaBoos
  • 5. Target Localization
    • Possible search space is xyt cube
    • Action needs to be localized in space and time
    • Target localization helps reducing search space
    • Background subtraction
    • ROI marked
    Original Video Silhouette Original Video with ROI marked
  • 6. Motion estimation
    • Make use of optical flows for motion estimation
    • Optical flow is the pattern of relative motion between the object/object feature points and the viewer/camera
    • We make use of Lucas – Kanade, two frame differential method, it comparatively yields robust and dense optical flows
  • 7. Noise Reduction
    • Noise removal by averaging
    • Optical flows with magnitude > C * O mean are ignored,
    • where C – constant [1.5 - 2],
    • O mean - mean of optical flow within ROI
  • 8.
    • Optical flows are aggregated near the motion
    • Need for representing optical flow in meaningful way
    • Fixed sized grid laid over the ROI
    Organizing optical flows
  • 9.
    • Magnitude and direction of Optical flows within each box b ij is averaged and assigned to its centre c ij
    • All optical flows have same weight
    Organizing optical flows (simple averaging)
  • 10.
    • Each optical flow given a weight
    • More the distance from the centre c ij less is the weight and vice-versa
    Organizing optical flows (weighted averaging)
  • 11.
    • Optical flows are arranged in structured mannered
    • Arranged optical flows are easier to analyze
    Organizing optical flows
  • 12. Shape discriptor
    • Shape gives information about the action
    • Viola-Jones box features used to get shape features
    • Shape information combined with motion information
  • 13. Spatio-temporal descriptor TLEN TSPAN
  • 14. Spatio-temporal descriptor
    • Shape and motion features combined over the span of time to form spatio-temporal features
  • 15. Learning with Adaboost
    • Adaboost is state of art learning algorithm
    • Linear decision stumps are used as weak hypothesis
    • Weak hypothesis combine to form a strong hypothesis
    • Strong hypothesis is weighted sum of weak hypothesis
    • Training and testing data is kept mutually exclusive
  • 16. Results
  • 17. Results (Weizman dataset)
  • 18. Thanks You