Ordered trajectories for large scale human action recognition
Recently, a video representation based on dense trajectories has been shown to outperform other human action recognition methods on several benchmark datasets. In dense trajectories, points are sampled at uniform intervals in space and time and then tracked using a dense optical flow field. The uniform sampling does not discriminate objects of interest from the background or other objects. Consequently, a lot of information is accumulated, which actually may not be useful. Sometimes, this...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of the IEEE International Conference on Computer Vision|
|01_Murthy_Ordered_trajectories_for_large_2013.pdf||430.47 kB||Adobe PDF||Request a copy|
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.