Long-range trajectories from global and local motion representations

dc.contributor.author Eduardo José Pereira en
dc.contributor.author Jaime Cardoso en
dc.contributor.author Morla,R en
dc.date.accessioned 2018-01-14T20:57:57Z
dc.date.available 2018-01-14T20:57:57Z
dc.date.issued 2016 en
dc.description.abstract Motion is a fundamental cue for scene analysis and human activity understanding in videos. It can be encoded in trajectories for tracking objects and for action recognition, or in form of flow to address behavior analysis in crowded scenes. Each approach can only be applied on limited scenarios. We propose a motion-based system that represents the spatial and temporal features of the flow in terms of long-range trajectories. The novelty resides on the system formulation, its generic approach to handle scene variability and motion variations, motion integration from local and global representations, and the resulting long-range trajectories that overcome trajectory-based approach problems. We report the results and conclusions that state its pertinence on different scenarios, comparing and correlating the extracted trajectories of individual pedestrians, manually annotated. We also propose an evaluation framework and stress the diverse system characteristics that can be used for human activity tasks, namely on motion segmentation. © 2016 Elsevier Inc. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/6083
dc.identifier.uri http://dx.doi.org/10.1016/j.jvcir.2016.06.020 en
dc.language eng en
dc.relation 3889 en
dc.relation 5573 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Long-range trajectories from global and local motion representations en
dc.type article en
dc.type Publication en
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