Human Action Recognition by Learning Bases of Action Attributes and Parts

1. 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

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

8. Experiments • PASCAL actions • Stanford 40 actions

9. PASCAL

10. Stanford 40 actions