human action recognition by learning bases of action attributes and parts bangpeng yao, xiaoye...
TRANSCRIPT
Human Action Recognition by Learning Bases of Action Attributes
and Parts
Bangpeng Yao, Xiaoye Jiang, Aditya Khosla, Andy Lai Lin, Leonidas Guibas, and Li Fei-Fei
Stanford University
Outline
• Introduction• Action Bases• Learning the Dual-Sparse Action Bases and
Reconstruction Coefficients• Experiments
Introduction
• Human action recognition in still images• A general image classification problem• Human-object interaction• Parts + Attributes
• Contributions• Represent each image by using a sparse set of
action bases that are meaningful to the content of the image
• Effectively learn these bases given far-from-perfect detections of action attributes and parts without meticulous human labeling
Action Bases
• Attributes and parts• Attributes: verb, learned
by discriminative classifiers
• Parts: object parts and poselets, learned by pre-trained object detectors and poselet detectors
• A vector of the normalized confidence scores obtained from these classifiers and detectors is used to represent this image.
Action Bases
• High-order interactions of image attributes and parts
• is used to represent each image and SVMs are trained for action classification
Dual-sparsity Learning
Experiments
• PASCAL actions• Stanford 40 actions
• PASCAL
• Stanford 40 actions