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Recognition and tracking of human body parts

Recognition and tracking of human body parts. Algirdas Beinaravičius Gediminas Mazrimas Salman Mosslem. Contents. Introduction Background subtraction techniques Image segmentation Color spaces Clustering Blobs Body part recognition Problems and conclusion. Introduction. Project tasks.

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Recognition and tracking of human body parts

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  1. Recognition and tracking of human body parts AlgirdasBeinaravičius Gediminas Mazrimas Salman Mosslem

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

  3. Introduction. Project tasks • Background subtraction/Foreground extraction • Color spaces and K-Means clustering • Blob-level introduction • Body part recognition

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

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

  6. Background subtraction“Codebook” model

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

  8. Image segmentation • Color spaces • RGB • HSI • I3 (Ohta) • YCC (LumaChroma) • Clustering • K-Means • Markov Random Field

  9. Image segmentationColor space: RGB • RGB (Red Green Blue) • Classical color space • 3 color channels (0-255) • In this project: • Used in background subtraction

  10. Image segmentationColor space: HSI • 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

  11. Image segmentationClustering • Image data (pixels) classification to distinct partitions (labeling problem) • Color space importance in clustering

  12. Image segmentationClustering: K-Means • 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:

  13. Image segmentationClustering: K-Means example

  14. Image segmentationClustering: K-Means Euclidean/Mahalanobis distance comparison Euclidean distance Mahalanobis distance

  15. Image segmentationClustering: K-Means color space comparison RGB HSI

  16. Image segmentationClustering: MRF • Probabilistic graphical model using prior knowledge • Usage: • Pixel-level • Blob level • Concepts from MRF: • Neighborhood system • Cliques

  17. Image segmentationClustering: MRF Neighborhood system Cliques

  18. Blobs • Blob parameters • Blob formation • Blob fusion conditions • Blob fusion

  19. Blobs • Higher level of abstraction • Ability to identify body parts • Faster processing

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

  21. BlobsInitial creation • Input: K-means image/matrix. • Output: Set of blobs

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

  23. BlobsFusion • 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. Body part recognition (I) • Associate blobs to body parts

  25. Body part recognition (II) • Skin blobs play the key role: • Head and Upper body: • Torso identification • Face and hands identification • Lower body: • Legs and feet identification

  26. Body part recognition (III)

  27. Problems (I) • Computational time • Background subtraction quality • Subject clothing • Subject position • Number of clusters in K-Means algorithm • Skin blobs

  28. Problems (II)

  29. Problems (III)

  30. Conclusion and future work • Main tasks completed • Improvements are required for better results • Possible future work: • Multiple people tracking • Detailed body part recognition

  31. Questions, comments ?

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