1. Algirdas Beinaravičius Gediminas Mazrimas Salman Mosslem

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

3.  Background subtraction/Foreground extraction  Color spaces and K-Means clustering  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

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

8.  Color spaces ◦ RGB ◦ HSI ◦ I3 (Ohta) ◦ YCC (Luma Chroma)  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 K clusters  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.  Blob parameters  Blob formation  Blob fusion conditions  Blob fusion

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

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

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

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

24.  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

25.  Associate blobs to body parts

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

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

31.  Main tasks completed  Improvements are required for better results  Possible future work: ◦ Multiple people tracking ◦ Detailed body part recognition

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