Dynamic Perceptual Attribute-Based Hidden Conditional Random Fields for Gesture Recognition

The demand for gesture/action recognition technologies has been increased in the recent years. State-of-the-art systems of gesture/action recognition have been using low-level features or intermediate bag-of-features as gesture/action descriptors. Those methods ignore the spatial and temporal information on shape and internal structures of the targets. Dynamic Perceptual Attributes (DPAs) is a set of descriptors of gesture's perceptual properties. Their context relations reveal gestures/actions' intrinsic structures. This paper utilizes the hidden conditional random fields (HCRF) model based on DPAs to describe complex human gestures and facilitate the recognition tasks. Experimental results show our model gains better performance against state-of-the-art methods.