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General human action recognition requires understanding of various visualcues. In this paper, we propose a network architecture that computes andintegrates the most important visual cues for action recognition: pose, motion,and the raw images. For the integration, we introduce a Markov chain modelwhich adds cues successively. The resulting approach is efficient andapplicable to action classification as well as to spatial and temporal actionlocalization. The two contributions clearly improve the performance overrespective baselines. The overall approach achieves state-of-the-art actionclassification performance on HMDB51, J-HMDB and NTU RGB+D datasets. Moreover,it yields state-of-the-art spatio-temporal action localization results onUCF101 and J-HMDB.

Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection