1 / 24

Human Action Recognition by Learning Bases of Action Attributes and Parts

Human Action Recognition by Learning Bases of Action Attributes and Parts. Outline. Introduction Action Recognition with Attributes & Parts Learning Experiments and Results. Introduction. use attributes and parts for recognizing human actions in still images

foy
Télécharger la présentation

Human Action Recognition by Learning Bases of Action Attributes and Parts

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Human Action Recognition by Learning Bases of Action Attributes and Parts

  2. Outline • Introduction • Action Recognition with Attributes & Parts • Learning • Experiments and Results

  3. Introduction • use attributes and partsfor recognizing human actions in still images • use the whole image to represent an action • treat action recognition as a general image classificationproblem • PASCAL challenge • spatial pyramid • random forest based methods • No explore the semantically meaningful components

  4. Introduction • some methods rely on labor-intensive annotations of objectsand human body parts during training time • Inspired by the recent work • usingobjectsand body parts for action recognition • propose an attributes and parts basedrepresentation • The action attributes are holistic image descriptions of human actions • associated with verbs in the human language • E.g. Riding,sitting,repairing,lifting…

  5. Introduction

  6. Introduction • a large number of possible interactions among these attributes parts in terms of co-occurrence statistics. • Our challenge is • represent image by using a sparse set of action bases • effectively learn these bases given far-from-perfect detections of action attributes • parts without meticulous human labeling as proposed in previous work

  7. Introduction • our method has theoretical foundations in sparse coding and compressed sensing . • PASCAL action dataset • Stanford 40 Actions dataset

  8. Attributes and Parts in Human Actions • Attribute: • Use are related to verbs in human language • E.x: rinding a bike can be “riding” and “sitting” • attribute to correspond to more than one action • Parts: • Composed of objects • Human poses

  9. Attributes and Parts in Human Actions • an action image consists • the objects that are closely related to the action • The descriptive local human poses. • A vector of the normalized confidence scores obtained from these classifiers and detectors is used to represent this image

  10. Action Bases of Attributes and Parts • Our method learns high-order interactions of image attributes and parts • carry richer information about human actions • improve recognition performance • Riding – sitting – bike • Using - keyboard - monitor - sitting

  11. Action Bases of Attributes and Parts • formalize the action bases in a mathematical framework • P: attributes and parts • 1 • Action bases: • Coefficients: • 4 • 5

  12. Action Classification Using the Action Bases • the attributes and parts representation A • reconstructed from the sparse factorization coefficients w. • use the coefficients vector w to represent an image • train an SVM classifier for action classification

  13. Learning the Dual-Sparse Action Bases and Reconstruction Coefficients • 1 • Ai is the vector of confidence scores • there exists a latent dictionary of bases • frequent co-occurrence of attributes • e.g. “cycling” and “bike” • To identify a set of sparse bases Φ = [𝝓1..𝝓M]

  14. Learning the Dual-Sparse Action Bases and Reconstruction Coefficients • learn the bases Φ and find the reconstruction coefficients wifor each ai . • (2) is non-convex,(3)is convex • Eqn.2 is convex with respect to each of the two variables Φ and W when the other one is fixed

  15. Learning the Dual-Sparse Action Bases and Reconstruction Coefficients • This is called the elastic-net constraint set[29] • λ= 0.1 • ϒ= 0.15

  16. Google, Bing, and Flickr • 180∼300 images for • each class

  17. Experiments and Results

  18. Experiments and Results

  19. Experiments and Results • PASCAL Stanford 40 action • attributes (A), objects (O), and poselets (P)

  20. Experiments and Results

  21. Discussion • use attributesand parts for action recognition • The attributes are verbs • The parts are composed of objects and poselets • reconstructed by a set of sparse coefficients • our method achieves state-of-the-art performance on two datasets

  22. Future work • learned action bases for image tagging • explore more detailed semantic understanding of human actions in images

More Related