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In this paper, we propose to improve the traditional use of RNNs by employinga many to many model for video classification. We analyze the importance ofmodeling spatial layout and temporal encoding for daily living actionrecognition. Many RGB methods focus only on short term temporal informationobtained from optical flow. Skeleton based methods on the other hand show thatmodeling long term skeleton evolution improves action recognition accuracy. Inthis work, we propose a deep-temporal LSTM architecture which extends standardLSTM and allows better encoding of temporal information. In addition, wepropose to fuse 3D skeleton geometry with deep static appearance. We validateour approach on public available CAD60, MSRDailyActivity3D and NTU-RGB+D,achieving competitive performance as compared to the state-of-the art.

Deep-Temporal LSTM for Daily Living Action Recognition