This paper presents a novel representation for human actions which encodes the variations in the shape and motion of the performing actor. When an actor performs an action, at eac...
In this paper, we present a novel approach for automatically learning a compact and yet discriminative appearance-based human action model. A video sequence is represented by a ba...
Abstract. We propose a methodology for recognizing actions at a distance by watching the human poses and deriving descriptors that capture the motion patterns of the poses. Human p...
In this paper, we propose a novel Spatiotemporal Interest Point (MC-STIP) detector based on the coherent motion pattern around each voxel in videos. Our detector defines the local...
This paper proposes after-action review (AAR) with Human-Virtual human (H-VH) experiences. H-VH experiences are seeing increased use in training for real-world, H-H experiences. T...