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Activity recognition by learning structural and pairwise mid-level features using random forest
2013
2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)
This paper presents a novel random forest based method to build mid-level features describing spatial and tem poral structure information for activity recognition. Our model consists of two separate parts, spatial part and temporal part, which are employed to capture the distinctive characteristics in spatial and temporal domains of activity analysis. In the spatial part, densely sampled low level features are passed through the first level random forest and concatenated structurally to form
doi:10.1109/fg.2013.6553706
dblp:conf/fgr/HuKF13
fatcat:fifvvznno5hwxmvaa4r2gakocu