Human interaction recognition using spatial-temporal salient feature

Depth sensor is widely used today and has great impact in object pose estimation, camera tracking, human actions, and scene reconstruction. This paper presents a novel method for human interaction recognition based on 3D skeleton data captured by Kinect sensor using hierarchical spatial-temporal saliency-based representation method. Hierarchical saliency can be conceptualized as Salient Actions at the highest level, determined by the initial movement in an interaction; Salient Points at middle level, determined by a single time point uniquely identified for all instances of Salient Action; Salient Joints at the lowest level, determined by the greatest positional changes of human joints in a Salient Action sequence. Given the interaction saliency at different levels, several types of features, such as spatial displacement, direction relations, and etc., are introduced based on action characteristics. Since there are few publicly accessible test datasets, we created a new dataset with eight types of interactions named K3HI, using the Microsoft Kinect. The method was tested based on Support Vector Machine (SVM) multi-class classifier. In the experiment, the results demonstrate that the average recognition accuracy of hierarchical saliency-based representation is 90.29%, outperforming methods using other features.