An effective fusion scheme of spatio-temporal features for human action recognition in RGB-D video

We investigate the problem of human action recognition by studying the effects of fusing feature streams retrieved from color and depth sequences. Our main contribution is two-fold: First, we present the so-called 3DS-HONV descriptor which is a spatio-temporal extension of Histogram of Oriented Normal vector (HONV), specifically designed for capturing the joint shape-motion vision cues from depth sequences; on the other hand, an effective RGB-D features fusion scheme, which exploits information from both color and depth channels, is developed to extract expressive representations for action recognition in real scenarios. As a result, despite its simplicity, our 3DS-HONV descriptor performs surprisingly well, and achieves the state-of-the-art performance on MSRAction3D dataset, which is 88.89% in overall accuracy. Further experiments demonstrate that our latter feature fusion scheme also generalizes well and achieves good results on the one-shot-learning ChaLearn Gesture Data (CGD2011).