  Motion is a fundamental cue for scene analysis and human activity understan-
ding in videos. It can be encoded in trajectories for tracking objects and for
action recognition, or in form of flow to address behaviour 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.
