1. AlgirdasBeinaravičius Gediminas Mazrimas Salman Mosslem

2.  Project introduction  Background subtraction techniques  Image segmentation ◦ Color spaces ◦ Clustering  Blobs  Body part recognition  Problems and conclusion

3.  Goal: recognition of human body parts for a subject from video sequence images  Background subtraction/Foreground extraction  K-Means clustering for color images  Blob-level introduction  Body part recognition

4.  What is background subtraction?  Background subtraction models: ◦ Gaussian model ◦ “Codebook” model

5.  Learning the model  Gaussian parameters estimation  Thresholds - Foreground/Background determination

6.  Non-parametric model

7. Original image Background subtraction using Gaussian model Background subtraction using Codebook model

8.  How important is image segmentation?  Color spaces ◦ RGB ◦ HSI ◦ I3 (Ohta), YCC (LumaChroma), HSV…  Clustering ◦ K-Means ◦ Markov Random Field

9. What does image segmentation? Why we needed different color spaces? What was clustering for?

10.  RGB (Red Green Blue) ◦ Classical color space ◦ 3 color channels (0-255)  In this project: ◦ Used in background subtraction

11.  HSI (Hue Saturation Intensity/Lightness) ◦ Similar to HSV (Hue Saturation Value) ◦ 3 color channels:  Hue – color itself  Saturation – color pureness  Intensity – color brightness ◦ Converted from normalized RGB values ◦ Intensity significance minimized  In this project: ◦ Used in clustering ◦ Blob formation ◦ Body part recognition

12.  Image data (pixels) classification to distinct partitions (labeling problem)  Color space importance in clustering

13.  Clustering without any prior knowledge  Working only with foreground image  Totally Kclusters  Classification based on cluster centroid and pixel value comparison ◦ Euclidean distance: ◦ Mahalanobis distance:

15. Euclidean distanceMahalanobis distance

16. RGBHSI

17.  Probabilistic graphical model using prior knowledge  Usage: ◦ Pixel-level ◦ Blob-level  Concepts from MRF: ◦ Neighborhood system ◦ Cliques

18. Neighborhood system Cliques

19.  Higher level of abstraction ◦ Ability to identify body parts ◦ Faster processing

20.  Label.  Set of area pixels.  Centroid.  Mean color value.  Set of pixels, forming convex hull.  Set of neighboring blobs.  Skin flag.

21.  Input: K-means image/matrix.  Output: Set of blobs

22.  Particularly important in human body part recognition.  Can not be fused.  Technique to identify skin blobs: ◦ Euclidean distance

23.  Conditions: ◦ Blobs have to be neighbors ◦ Blobs have to share a large border ratio ◦ Blobs have to be of similar color ◦ Small blobs are fused to their largest neighbor  Neither of these conditions apply to skin blobs

24.  Associate blobs to body parts

25.  Skin blobs play the key role: ◦ Head and Upper body:  Torso identification  Face and hands identification ◦ Lower body:  Legs and feet identification

27.  Computational time  Background subtraction sensitivity  Subject clothing  Subject position  Number of clusters in K-Means algorithm  Skin blobs

30.  Main tasks completed  Improvements are required for better results  Possible future work: ◦ Multiple people tracking ◦ Detailed body part recognition ◦ Algorithm improvements with better computer hardware usage for live video images

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