Online context-based person re-identification and biometric-based action recognition for service robots

In this paper, we address the problem of person re-identification and action recognition for service robots, which undergoes lack of training dataset for model learning, reduction of feature set discriminative power in changing scenarios, and high complexity of the algorithm computation. An online context-based person re-identification algorithm is proposed, which learns the person model online without pre-collect dataset and adjusts the weight of features according to the context information. An online biometric-based action recognition algorithm is proposed, actions are recognized by simply matching the skeleton vectors extracted from five linkage mechanisms of human body. The proposed algorithms are evaluated on a service robot system, extensive experimental results show that they performs efficiently and effectively in various real-life scenarios.