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Human Action Recognition by Learning Bases of Action Attributes and Parts Bangpeng Yao, Xiaoye Jiang, Aditya Khosla, Andy Lai Lin, Leonidas Guibas, and.

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Presentation on theme: "Human Action Recognition by Learning Bases of Action Attributes and Parts Bangpeng Yao, Xiaoye Jiang, Aditya Khosla, Andy Lai Lin, Leonidas Guibas, and."— Presentation transcript:

1 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

2 Outline Introduction Action Bases Learning the Dual-Sparse Action Bases and Reconstruction Coefficients Experiments

3 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

4 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.

5 Action Bases High-order interactions of image attributes and parts is used to represent each image and SVMs are trained for action classification

6 Dual-sparsity Learning

7

8 Experiments PASCAL actions Stanford 40 actions

9 PASCAL

10 Stanford 40 actions


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