Action Recognition Using Discriminative Structured Trajectory Groups

Indriyati Atmosukarto, Narendra Ahuja, Bernard Ghanem

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

12 Scopus citations


In this paper, we develop a novel framework for action recognition in videos. The framework is based on automatically learning the discriminative trajectory groups that are relevant to an action. Different from previous approaches, our method does not require complex computation for graph matching or complex latent models to localize the parts. We model a video as a structured bag of trajectory groups with latent class variables. We model action recognition problem in a weakly supervised setting and learn discriminative trajectory groups by employing multiple instance learning (MIL) based Support Vector Machine (SVM) using pre-computed kernels. The kernels depend on the spatio-temporal relationship between the extracted trajectory groups and their associated features. We demonstrate both quantitatively and qualitatively that the classification performance of our proposed method is superior to baselines and several state-of-the-art approaches on three challenging standard benchmark datasets.
Original languageEnglish (US)
Title of host publication2015 IEEE Winter Conference on Applications of Computer Vision
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Print)9781479966837
StatePublished - Feb 24 2015

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


Dive into the research topics of 'Action Recognition Using Discriminative Structured Trajectory Groups'. Together they form a unique fingerprint.

Cite this