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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 Li Fei-Fei Stanford University. Outline. Introduction Action Bases
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Human Action Recognition by Learning Bases of Action Attributes and Parts Bangpeng Yao, Xiaoye Jiang, AdityaKhosla, Andy Lai Lin, LeonidasGuibas, 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
Experiments • PASCAL actions • Stanford 40 actions