Recognizing human actions from still images with latent poses
- 1 June 2010
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 2030-2037
- https://doi.org/10.1109/cvpr.2010.5539879
Abstract
We consider the problem of recognizing human actions from still images. We propose a novel approach that treats the pose of the person in the image as latent variables that will help with recognition. Different from other work that learns separate systems for pose estimation and action recognition, then combines them in an ad-hoc fashion, our system is trained in an integrated fashion that jointly considers poses and actions. Our learning objective is designed to directly exploit the pose information for action recognition. Our experimental results demonstrate that by inferring the latent poses, we can improve the final action recognition results.Keywords
This publication has 15 references indexed in Scilit:
- Learning actions from the WebPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- Discriminative models for multi-class object layoutPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- Large margin training for hidden Markov models with partially observed statesPublished by Association for Computing Machinery (ACM) ,2009
- Pose search: Retrieving people using their posePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- A discriminatively trained, multiscale, deformable part modelPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2008
- Learning realistic human actions from moviesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2008
- Pose primitive based human action recognition in videos or still imagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2008
- Unsupervised Learning of Human Action Categories Using Spatial-Temporal WordsPublished by British Machine Vision Association and Society for Pattern Recognition ,2006
- Histograms of Oriented Gradients for Human DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Recognizing human actions: a local SVM approachPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004