Human Action Recognition Using Stereo Trajectories

This paper proposes a new method that uses a pair of uncalibrated stereo videos, without the need for three-dimensional reconstruction, for human action recognition (HAR). Two stereo views of the same scene, obtained from two different cameras, are used to create a set of two-dimensional trajectories. Then, we calculate disparities between them and fuse them with the trajectories, to obtain our disparity-augmented trajectories that is used in our HAR method. The obtained results have shown on average a 2.40% improvement, when using disparity-augmented trajectories, compared to using the classical 2D trajectory information only. Furthermore, we have also tested our method on the challenging Hollywood 3D dataset and, we have obtained competitive results, at a faster speed than some state of the art methods.