Camera Motion and Surrounding Scene Appearance as Context for Action Recognition

Fabian Caba Heilbron, Ali Kassem Thabet, Juan Carlos Niebles, Bernard Ghanem

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

5 Scopus citations


This paper describes a framework for recognizing human actions in videos by incorporating a new set of visual cues that represent the context of the action. We develop a weak foreground-background segmentation approach in order to robustly extract not only foreground features that are focused on the actors, but also global camera motion and contextual scene information. Using dense point trajectories, our approach separates and describes the foreground motion from the background, represents the appearance of the extracted static background, and encodes the global camera motion that interestingly is shown to be discriminative for certain action classes. Our experiments on four challenging benchmarks (HMDB51, Hollywood2, Olympic Sports, and UCF50) show that our contextual features enable a significant performance improvement over state-of-the-art algorithms.
Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science
PublisherSpringer Nature
Number of pages15
ISBN (Print)9783319168166
StatePublished - Apr 17 2015

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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