recognition and tracking of human body parts
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
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Recognition and tracking of human body parts
Algirdas BeinaravičiusGediminas Mazrimas
Salman Mosslem
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Introduction Background subtraction techniques Image segmentation
◦ Color spaces◦ Clustering
Blobs Body part recognition Problems and conclusion
Contents
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Background subtraction/Foreground extraction
Color spaces and K-Means clustering 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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Background subtraction“Codebook” model
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Background subtractionModel comparison
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Color spaces◦ RGB◦ HSI◦ I3 (Ohta)◦ YCC (Luma Chroma)
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 K clusters 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 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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Blob parameters Blob formation Blob fusion conditions Blob fusion
Blobs
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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 quality 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
Conclusion and future work
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?
Questions, comments