Ordered trajectories for human action recognition with large number of classes
Recently, a video representation based on dense trajectories has been shown to outperform other human action recognition methods on several benchmark datasets. The trajectories capture the motion characteristics of different moving objects in space and temporal dimensions. In dense trajectories, points are sampled at uniform intervals in space and time and then tracked using a dense optical flow field over a fixed length of L frames (optimally 15) spread overlapping over the entire video....[Show more]
|Collections||ANU Research Publications|
|Source:||Image and Vision Computing|
|01_Murthy_Ordered_trajectories_for_human_2015.pdf||2.38 MB||Adobe PDF||Request a copy|
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