Range-Sample Depth Feature for Action Recognition

We propose binary range-sample feature in depth. It is based on τ tests and achieves reasonable invariance with respect to possible change in scale, viewpoint, and background. It is robust to occlusion and data corruption as well. The descriptor works in a high speed thanks to its binary property. Working together with standard learning algorithms, the proposed descriptor achieves state-of-the-art results on benchmark datasets in our experiments. Impressively short running time is also yielded.