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Recently, deep learning approach has achieved promising results in variousfields of computer vision. In this paper, a new framework called HierarchicalDepth Motion Maps (HDMM) + 3 Channel Deep Convolutional Neural Networks(3ConvNets) is proposed for human action recognition using depth map sequences.Firstly, we rotate the original depth data in 3D pointclouds to mimic therotation of cameras, so that our algorithms can handle view variant cases.Secondly, in order to effectively extract the body shape and motioninformation, we generate weighted depth motion maps (DMM) at several temporalscales, referred to as Hierarchical Depth Motion Maps (HDMM). Then, threechannels of ConvNets are trained on the HDMMs from three projected orthogonalplanes separately. The proposed algorithms are evaluated on MSRAction3D,MSRAction3DExt, UTKinect-Action and MSRDailyActivity3D datasets respectively.We also combine the last three datasets into a larger one (called CombinedDataset) and test the proposed method on it. The results show that our approachcan achieve state-of-the-art results on the individual datasets and withoutdramatical performance degradation on the Combined Dataset.

Deep Convolutional Neural Networks for Action Recognition Using Depth Map Sequences