Body Surface Context: A New Robust Feature for Action Recognition From Depth Videos

Human action recognition in videos is useful for many applications. However, there still exist huge challenges in real applications due to the variations in the appearance, lighting condition and viewing angle, of the subjects. In this consideration, depth data have advantages over red, green, blue (RGB) data because of their spatial information about the distance between object and viewpoint. Unlike existing works, we utilize the 3-D point cloud, which contains points in the 3-D real-world coordinate system to represent the external surface of human body. Specifically, we propose a new robust feature, the body surface context (BSC), by describing the distribution of relative locations of the neighbors for a reference point in the point cloud in a compact and descriptive way. The BSC encodes the cylindrical angular of the difference vector based on the characteristics of human body, which increases the descriptiveness and discriminability of the feature. As the BSC is an approximate object-centered feature, it is robust to transformations including translations and rotations, which are very common in real applications. Furthermore, we propose three schemes to represent human actions based on the new feature, including the skeleton-based scheme, the random-reference-point scheme, and the spatial-temporal scheme. In addition, to evaluate the proposed feature, we construct a human action dataset by a depth camera. Experiments on three datasets demonstrate that the proposed feature outperforms RGB-based features and other existing depth-based features, which validates that the BSC feature is promising in the field of human action recognition.