Dense Body Part Trajectories for Human Action Recognition

Ramana ORUGANTI, Ibrahim Hamed Ismail RADWAN, Roland GOECKE

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

4 Citations (Scopus)
3 Downloads (Pure)


Several techniques have been proposed for human action recognition from videos. It has been observed that incorporating mid-level viz. human body and/or high-level information viz. pose estimation in the computation of low-level features viz. trajectories yields the best performance in action recognition where full body is presumed. However, in datasets with a large number of classes, where the full body may not be visible at all times, incorporating such mid- and high-level information is unexplored. Moreover, changes and developments in any stage will require a recompute of all low-level features. We decouple mid-level and low-level feature computation and study on benchmark action recognition datasets such as UCF50, UCF101 and HMDB51, containing the largest number of action classes to date. Further, we employ a part-based model for human body part detection in frames statically, thus also investigating classes where the full body is not present. We also track dense regions around the detected human body parts by Hungarian particle linking,
thus minimising most of the wrongly detected body parts and enriching the mid-level information.
Original languageEnglish
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
Editors Pesquet-Popescu, Fowler
Place of PublicationParis, France
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781479957514
ISBN (Print)9781479957514
Publication statusPublished - 2014
EventIEEE International Conference on Image Processing 2014 - Paris, Paris, France
Duration: 27 Oct 201430 Oct 2014

Publication series

Name2014 IEEE International Conference on Image Processing, ICIP 2014


ConferenceIEEE International Conference on Image Processing 2014


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