3D CNNs on Distance Matrices for Human Action Recognition
Proceedings of the 2017 ACM on Multimedia Conference - MM '17
In this paper we are interested in recognizing human actions from sequences of 3D skeleton data. For this purpose we combine a 3D Convolutional Neural Network with body representations based on Euclidean Distance Matrices (EDMs), which have been recently shown to be very e ective to capture the geometric structure of the human pose. One inherent limitation of the EDMs, however, is that they are de ned up to a permutation of the skeleton joints, i.e., randomly shu ing the ordering of the joints
... ring of the joints yields many di erent representations. In oder to address this issue we introduce a novel architecture that simultaneously, and in an end-to-end manner, learns an optimal transformation of the joints, while optimizing the rest of parameters of the convolutional network. e proposed approach achieves state-of-the-art results on 3 benchmarks, including the recent NTU RGB-D dataset, for which we improve on previous LSTM-based methods by more than 10 percentage points, also surpassing other CNN-based methods while using almost 1000 times fewer parameters.