3D GLOH features for human action recognition

Human action recognition from videos has wide applicability and receives significant interests. In this work, to better identify spatio-temporal characteristics, we propose a novel 3D extension of Gradient Location and Orientation Histograms, which provides discriminative local features representing not only the gradient orientation, but also their relative locations. We further propose a human action recognition system based on the Bag of Visual Words model, by combining the new 3D GLOH local features with Histograms of Oriented Optical Flow (HOOF) global features. Along with the idea from our recent work to extract features only in salient regions, our overall system outperforms existing feature descriptors for human action recognition for challenging real-world video datasets.