Features and Classification Schemes for View-Invariant and Real-Time Human Action Recognition

Human action recognition (HAR) is largely used in the field of ambient assisted living to create an interaction between humans and computers. In these applications, it cannot be asked for people to act nonnaturally. The algorithm has to adapt and the interaction has to be as quick as possible to make it fluent. To improve the existing algorithms with regards to these points, we propose a novel method based on skeleton information provided by RGB-D cameras. This approach is able to carry out early action recognition and is more robust to viewpoint variability. To reach this goal, a new descriptor called body directional velocity is proposed and a real-time classification is performed. Experimental results on four benchmarks show that our method competes with various skeleton-based HAR algorithms. We also show the suitability of our method for early recognition of human actions.