AlgirdasBeinaravičius Gediminas Mazrimas Salman Mosslem.
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Transcript of AlgirdasBeinaravičius Gediminas Mazrimas Salman Mosslem.
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Recognition and tracking of human body parts
AlgirdasBeinaravičiusGediminas Mazrimas
Salman Mosslem
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Project introduction Background subtraction techniques Image segmentation
◦ Color spaces◦ Clustering
Blobs Body part recognition Problems and conclusion
Contents
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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
Introduction. Project tasks
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What is background subtraction? Background subtraction models:
◦ Gaussian model◦ “Codebook” model
Background subtraction
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Learning the model Gaussian parameters estimation
Thresholds - Foreground/Background determination
Background subtractionGaussian model
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Non-parametric model
Background subtraction“Codebook” model
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Background subtractionModel comparison
Original image
Background subtractionusing Gaussian model
Background subtractionusing Codebook model
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How important is image segmentation?
Color spaces◦ RGB◦ HSI◦ I3 (Ohta), YCC (LumaChroma), HSV…
Clustering◦ K-Means◦ Markov Random Field
Image segmentation
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RGB (Red Green Blue)◦ Classical color space◦ 3 color channels (0-255)
In this project:◦ Used in background subtraction
Image segmentationColor space: RGB
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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
Image segmentationColor space: HSI
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Image data (pixels) classification to distinct partitions (labeling problem)
Color space importance in clustering
Image segmentationClustering
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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:
Image segmentationClustering: K-Means
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Image segmentationClustering: K-Means example
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Image segmentationClustering: K-Means Euclidean/Mahalanobis distance comparison
Euclidean distance Mahalanobis distance
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Image segmentationClustering: K-Means color space comparison
RGB HSI
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Probabilistic graphical model using prior knowledge
Usage:◦ Pixel-level◦ Blob-level
Concepts from MRF:◦ Neighborhood system◦ Cliques
Image segmentationClustering: MRF
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Image segmentationClustering: MRFNeighborhood system
Cliques
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Higher level of abstraction◦ Ability to identify body parts◦ Faster processing
Blobs
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Label. Set of area pixels. Centroid. Mean color value. Set of pixels, forming convex hull. Set of neighboring blobs. Skin flag.
BlobsParameters
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Input: K-means image/matrix. Output: Set of blobs
BlobsInitial creation
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Particularly important in human body part recognition.
Can not be fused. Technique to identify skin blobs:
◦ Euclidean distance
BlobsSkin blobs
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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
BlobsFusion
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Associate blobs to body parts
Body part recognition (I)
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Skin blobs play the key role:◦ Head and Upper body:
Torso identification Face and hands identification
◦ Lower body: Legs and feet identification
Body part recognition (II)
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Body part recognition (III)
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Computational time Background subtraction sensitivity Subject clothing Subject position Number of clusters in K-Means algorithm Skin blobs
Problems (I)
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Problems (II)
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Problems (III)
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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
Conclusion and future work
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?
Questions, comments