Robust Action Recognition and Segmentation with Multi-Task Conditional Random Fields

In this paper, we propose a robust recognition and segmentation method for daily actions with a novel multi-task sequence labeling algorithm called multi-task conditional random field (MT-CRF). Multi-Task sequence labeling is a task of assigning input sequence to sequence of multi-labels that consist of one or multiple symbols in single frame. Multi-Task sequence labeling is essential for action recognition, since motions can be often classified into multi-labels, e.g. he is folding arms while sitting. The MT-CRFs: extensions of conditional random fields (CRFs), incorporate jointly interaction between action labels as well as Markov property of actions, to improve the performance of the joint accuracy: the accuracy for whole labels at specific time. The MT-CRFs offer several advantages over the generative dynamic Bayesian networks (DBNs), which are often utilized as multi-task sequence labelers. First, the MT-CRFs allow relaxing the strong assumption of conditional independence of observed motion, which is used in DBNs. Second, the MT-CRFs exploit the power of non-Markovian discriminative classification frameworks instead of generative models in DBNs. With deep insight of the problem Multi-Task sequence labeling, the inference process of the classifier gains more efficiency than the previous Markov random fields that tackle multi-task sequence labeling. The experimental results show that classifiers with MT-CRFs have better performance than cascaded classifiers with a couple of CRFs.