Introduction - If you have any usage issues, please Google them yourself
This paper advances prior work by proposing a joint learning
framework to simultaneously identify the spatial and temporal
extents of the action of interest in training videos. To get pixel-level
localization results, our method uses dense trajectories extracted
videos as local features to represent actions. We first
present a trajectory split-and-merge algorithm to segment a video
into the background and several separated foreground moving
objects. In this algorithm, the inherent temporal smoothness
of human actions is exploited to facilitate segmentation. Then,
with the latent SVM framework on segmentation results, spatial
and temporal extents of the action of interest are treated as
latent variables that are inferred simultaneously with action
recognition. Experiments on two challenging datasets show that
action detection with our learned spatial and temporal extents is
superior than state-of-the-art methods.