  The Dense Trajectories concept is one of the most successful approaches in
action recognition, suitable for scenarios involving a significant amount of
motion. However, due to noise and background motion, many generated
trajectories are irrelevant to the actual human activity and can potentially
lead to performance degradation. In this paper, we propose Localized
Trajectories as an improved version of Dense Trajectories where motion
trajectories are clustered around human body joints provided by RGB-D cameras
and then encoded by local Bag-of-Words. As a result, the Localized Trajectories
concept provides a more discriminative representation of actions as compared to
Dense Trajectories. Moreover, we generalize Localized Trajectories to 3D by
using the modalities offered by RGB-D cameras. One of the main advantages of
using RGB-D data to generate trajectories is that they include radial
displacements that are perpendicular to the image plane. Extensive experiments
and analysis are carried out on five different datasets.
