Poselet Key-Framing: A Model for Human Activity Recognition

Project Members

Michalis Raptis (Disney Research Pittsburgh)
Leonid Sigal (Disney Research Pittsburgh)


 Poselet Key-framing- A Model for Human Activity Recognition-Image

In this paper, we develop a new model for recognizing human actions. An action is modeled as a very sparse sequence of temporally local discriminative keyframes – collections of partial key-poses of the actor(s), depicting key states in the action sequence. We cast the learning of keyframes in a max-margin discriminative framework, where we treat keyframes as latent variables. This allows us to (jointly) learn a set of most discriminative keyframes while also learning the local temporal context between them. Keyframes are encoded using a spatially-localizable poselet-like representation with HoG and BoW components learned from weak annotations; we rely on structured SVM formulation to align our components and mine for hard negatives to boost localization performance. This results in a model that supports spatio-temporal localization and is insensitive to dropped frames or partial observations. We show classification performance that is competitive with the state of the art on the benchmark UT-Interaction dataset and illustrate that our model outperforms prior methods in an on-line streaming setting.


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Poselet Key-framing: A Model for Human Activity Recognition
June 25, 2013
IEEE Conference on Computer Vision Pattern Recognition (CVPR) 2013
Paper File [pdf, 5.02 MB]

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