On the improvement of human action recognition from depth map sequences using Space-Time Occupancy Patterns

摘要

We present a new visual representation for 3D action recognition from sequences of depth maps. In this new representation, space and time axes are divided into multiple segments to define a 4D grid for each depth map sequences. Each cell in the grid is associated with an occupancy value which is a function of the number of space-time points falling into this cell. The occupancy values of all the cells form a high dimensional feature vector, called Space-Time Occupancy Pattern (STOP). We then perform dimensionality reduction to obtain lower-dimensional feature vectors. The advantage of STOP is that it preserves spatial and temporal contextual information between space and time cells while being flexible enough to accommodate intra-action variations. Furthermore, we combine depth maps with skeletons in order to obtain view invariance and present an automatic segmentation and time alignment method for on-line recognition of depth sequences. Our visual representation is validated with experiments on a public 3D human action dataset.