Three-dimensional action recognition using volume integrals

Abstract

This work proposes the volume integral (VI) as a new descriptor for three-dimensional action recognition. The descriptor transforms the actor's volumetric information into a two-dimensional representation by projecting the voxel data to a set of planes that maximize the discrimination of actions. Our descriptor significantly reduces the amount of data of the three-dimensional representations yet preserves the most important information. As a consequence, the action recognition process is greatly speeded up while achieving very high success rates. The method proposed is therefore especially appropriate for applications in which limitations of computing power and space are significant aspects to consider, such as real-time applications or mobile devices. Additionally, the descriptor is sensitive to reflected actions, i.e., same actions performed with different limbs can be differentiated. This paper tests the VI using several Dimensionality Reduction techniques (namely PCA, 2D-PCA, LDA) and different Machine Learning approaches (namely Clustering, SVM and HMM) so as to determine the best combination of these for the action recognition task. Experiments conducted on the public IXMAS dataset show that the VI compares favorably with state-of-the-art descriptors both in terms of classification rates and computing times.

Application of Neural Network for Human Actions Recognition

“…Further, they formulate the task of multi-view human action recognition as a learning problem penalized by a graph structure that is built according to the human body structure. Work [4] proposes the volume integral as a new descriptor for threedimensional action recognition. The descriptor transforms the actor's volumetric information into a two-dimensional representation by projecting the voxel data to a set of planes that maximize the discrimination of actions.…”

Multiview Layer Fusion Model for Action Recognition Using RGBD Images

“…These methods do not require accurate background subtractions but make use of acquired, inconstant features that need strategy and descriptors to manage. Volume-based representations are modeled by stacks of silhouettes, shapes, or surfaces that use several frames to build a model, such as space-time silhouettes from shape history volume [ 32 ], geometric properties from continuous volume [ 33 ], spatial-temporal shapes from 3D point clouds [ 34 ], spatial-temporal features of shapelets from 3D binary cube space-time [ 35 ], affine invariants with SVM [ 36 ], spatial-temporal micro volume using binary silhouettes [ 37 ], integral volume of visual-hull and motion history volume [ 38 ], and saliency volume from luminance, color, and orientation components [ 39 ]. These methods acquire a detailed model but must deal with high dimensions of features which require accurate human segmentation without the background.…”