NDLI: Learning Generative Models for Multi-Activity Body Pose Estimation

Content Provider	Springer Nature Link
Author	Jaeggli, Tobias Koller Meier, Esther Gool, Luc
Copyright Year	2008
Abstract	We present a method to simultaneously estimate 3D body pose and action categories from monocular video sequences. Our approach learns a generative model of the relationship of body pose and image appearance using a sparse kernel regressor. Body poses are modelled on a low-dimensional manifold obtained by Locally Linear Embedding dimensionality reduction. In addition, we learn a prior model of likely body poses and a dynamical model in this pose manifold. Sparse kernel regressors capture the nonlinearities of this mapping efficiently. Within a Recursive Bayesian Sampling framework, the potentially multimodal posterior probability distributions can then be inferred. An activity-switching mechanism based on learned transfer functions allows for inference of the performed activity class, along with the estimation of body pose and 2D image location of the subject. Using a rough foreground segmentation, we compare Binary PCA and distance transforms to encode the appearance. As a postprocessing step, the globally optimal trajectory through the entire sequence is estimated, yielding a single pose estimate per frame that is consistent throughout the sequence. We evaluate the algorithm on challenging sequences with subjects that are alternating between running and walking movements. Our experiments show how the dynamical model helps to track through poorly segmented low-resolution image sequences where tracking otherwise fails, while at the same time reliably classifying the activity type.
Starting Page	121
Ending Page	134
Page Count	14
File Format	PDF
ISSN	09205691
Journal	International Journal of Computer Vision
Volume Number	83
Issue Number	2
e-ISSN	15731405
Language	English
Publisher	Springer US
Publisher Date	2008-07-31
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Monocular pose estimation Machine learning Dimensionality reduction Activity recognition Human locomotion Pattern Recognition Image Processing and Computer Vision Artificial Intelligence (incl. Robotics) Computer Imaging, Vision, Pattern Recognition and Graphics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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