Human action recognition via multi-scale 3D stationary wavelet analysis

Multi-scale methods, especially wavelets, are being used in various computer vision applications, including surveillance, robotics, and human-centered computing. Human action recognition is one of the core areas that dominate the aforementioned applications. In this paper, the 3D multi-scale stationary wavelet analysis is used to build a view-based multi-scale spatio-temporal representation of the human actions. The proposed representation benefits from the ability of the 3D stationary wavelet transform to fuse the spatio-temporal information highlighted at different scales and orientations. Experimental results using Weizmann and KTH datasets revealed a good performance in various scenarios with different conditions.