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Structural Human Action Recognition from Still Images
Moin Nabi
Computer Vision Lab.
©IPM - Oct. 2010
Problem Definition
How can we recognize human action from a single Image?
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Problem Definition
Pose as a Latent Valiable
Application
• News/sports image retrieval and analysis• An important cue for video-based action
recognition
Previous Works• Global template-based representation
HOG by Dalal and Triggs. And , Ikizler-Cinbis et al. ICCV09
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Action Label
Action Label
• Pose estimation -> action recognitione.g. Ramanan and Forsyth NIPS03, Ferrari et al. CVPR09
Our Work
• Examplar based representation
Using Poselet as a new definition of a part
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• Pose estimation + action recognition
Discriminative Pose
Golfing?
Walking?
• All elements of pose are not equally important• Develop integrated learning framework to
estimate pose for action recognition
Pose Representation
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• We use a coarse non-parametric pose representation– An action-specific variant of the poselet[Bourdev&Malik ICCV09]
• A poselet is a set of patches not only with similar pose configuration, but also from the same action class.
Poselets
• Poselets obtained by clustering ground-truth joint positions of body parts for each action
PoseletsVisualization of the Poselets for Running images
For Every Action Class:
1. Devide annotation to 4 parts2. Cluster on normalized x,y3. Remove small clusters4. Crop that part of image
Learn SVM for every Poselet with HoG+: From that action -:same part from other action
5 (Actions) x 4 (Parts) x 5 (Clusters) = 100 – 10 (Remove) = 90 = 26 (leg) + 20 (L-arm) + 20 (R-arm) + 24 (Upper body)
Model Formation
⌂ Using Pictorial Structure Model of Pedro Felzenswalb
Training: Test:
Model Formation• Develop a scoring function– Should have high score for correct action label– Low score for other action labels– Model parameters
Model Formation
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Pose
Action Label
Image
Choose best pose L
Model Formation
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Pose
Action Label
Image
Running
Large score for
Model Formation
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Pose
Action Label
Image
Sitting
Small score for
Model Formulation
Pairwise Relation
Part Appearance
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Part Appearance Potential
Pose
Action Label
Image
Poselet matching
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Pairwise Potential
Pose
Action Label
Image
Relative body part locations
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Full Model
Pose
Action Label
Image
Model parameters learned using max-margin
Learning and Inference
Latent SVM
We Should Minimize Loss Function !
Latent SVM
Results
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• Still image action dataset (Internet Image)
– Five action categories– 2458 images total– Train using 1/3 of images from each category
Visualization of latent pose
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Successful classification examples
Unsuccessful classification examples
Any question
?