Conference Proceedings


O'Connor Noel E.
Little Suzanne
Iveel Jargalsaikhan


Computer Science

algorithm feature representation machine learning low level features video human localization action localisation recognition

Action localization in video using a graph-based feature representation (2017)

Abstract We propose a new framework for human action localization in video sequences. The option to not only detect but also localize actions in surveillance video is crucial to improving system's ability to manage high volumes of CCTV. In the approach, the action localization task is formulated the maximum-path finding problem in the directed spatio-temporal video-graph. The graph is constructed on the top of frame and temporal-based low-level features. To localize actions in the video-graph, we apply a maximum-path algorithm to find the path in the graph that is considered to be the localized action in the video. The proposed approach achieves competitive performance with the J-HMDB and the UCF-Sports dataset.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: INSIGHT Centre for Data Analytics
Ireland -> Dublin City University -> Subject = Computer Science: Machine learning

Full list of authors on original publication

O'Connor Noel E., Little Suzanne, Iveel Jargalsaikhan

Experts in our system

Iveel Jargalsaikhan
Dublin City University
Total Publications: 9