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