Sparse representation of point trajectories for action classification

Ravishankar Sivalingam, Guruprasad Somasundaram, Vineet Bhatawadekar, Vassilios Morellas, Nikolaos P Papanikolopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Action classification is an important component of human-computer interaction. Trajectory classification is an effective way of performing action recognition with significant success reported in the literature. We compare two different representation schemes, raw multivariate time-series data and the covariance descriptors of the trajectories, and apply sparse representation techniques for classifying the various actions. The features are sparse coded using the Orthogonal Matching Pursuit algorithm, and the gestures and actions are classified based on the reconstruction residuals. We demonstrate the performance of our approach on standardized datasets such as the Australian Sign Language (AusLan) and UCF Motion Capture datasets, collected using high-quality motion capture systems, as well as motion capture data obtained from a Microsoft Kinect sensor.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Robotics and Automation, ICRA 2012
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3601-3606
Number of pages6
ISBN (Print)9781467314039
DOIs
StatePublished - Jan 1 2012
Event 2012 IEEE International Conference on Robotics and Automation, ICRA 2012 - Saint Paul, MN, United States
Duration: May 14 2012May 18 2012

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Other

Other 2012 IEEE International Conference on Robotics and Automation, ICRA 2012
CountryUnited States
CitySaint Paul, MN
Period5/14/125/18/12

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