Two-person Activity Recognition using Skeleton Data

Human activity recognition is an important and active field of research having a wide range of application in numerous fields, including ambient assisted living. Although most of the researches are focused on the single user, the ability to recognize two-person interactions is perhaps more important for its social implications. This paper presents a two-person activity recognition system that uses skeleton data extracted from a depth camera. The human actions are encoded using a set of a few basic postures obtained with an unsupervised clustering approach. Multiclass Support Vector Machines (SVMs) are used to build models on the training set, while the X-means algorithm is employed to dynamically find the optimal number of clusters for each sample during the classification phase. The system is evaluated on the ISR-UoL and SBU datasets, reaching an overall accuracy of 0.87 and 0.88 respectively. Although the results show that the performances of the system are comparable with the state-of-the-art, recognition improvements are obtained with the activities related to health-care environments, showing promise for applications in the assisted-living realm.