We propose a novel system for unsupervised skeleton-based action recognition.
Given inputs of body keypoints sequences obtained during various movements, our
system associates the sequences with actions. Our system is based on an
encoder-decoder recurrent neural network, where the encoder learns a separable
feature representation within its hidden states formed by training the model to
perform prediction task. We show that according to such unsupervised training
the decoder and the encoder self-organize their hidden states into a feature
space which clusters similar movements into the same cluster and distinct
movements into distant clusters. Current state-of-the-art methods for action
recognition are strongly supervised, i.e., rely on providing labels for
training. Unsupervised methods have been proposed, however, they require camera
and depth inputs (RGB+D) at each time step. In contrast, our system is fully
unsupervised, does not require labels of actions at any stage, and can operate
with body keypoints input only. Furthermore, the method can perform on various
dimensions of body keypoints (2D or 3D) and include additional cues describing
movements. We evaluate our system on three extensive action recognition
benchmarks with different number of actions and examples. Our results
outperform prior unsupervised skeleton-based methods, unsupervised RGB+D based
methods on cross-view tests and while being unsupervised have similar
performance to supervised skeleton-based action recognition.

我们提出了一种新颖的无监督骨架动作识别系统，该系统基于编码器 - 解码器递归神经网络，可以无需提供任何标签或摄像头深度输入，并可在各维度的身体关键点（2D 或 3D）和附加提示描述移动的情况下操作，并将其与最新的无监督骨架方法进行比较。方法表现出更好的交叉视图性能，且与有监督骨架动作识别表现相似。

基于骨架的非监督动作识别和聚类

PREDICT & CLUSTER: Unsupervised Skeleton Based Action Recognition

This paper presents a novel approach for predicting the falls of people in
advance from monocular video. First, all persons in the observed frames are
detected and tracked with the coordinates of their body keypoints being
extracted meanwhile. A keypoints vectorization method is exploited to eliminate
irrelevant information in the initial coordinate representation. Then, the
observed keypoint sequence of each person is input to the pose prediction
module adapted from sequence-to-sequence(seq2seq) architecture to predict the
future keypoint sequence. Finally, the predicted pose is analyzed by the falls
classifier to judge whether the person will fall down in the future. The pose
prediction module and falls classifier are trained separately and tuned jointly
using Le2i dataset, which contains 191 videos of various normal daily
activities as well as falls performed by several actors. The contrast
experiments with mainstream raw RGB-based models show the accuracy improvement
of utilizing body keypoints in falls classification. Moreover, the precognition
of falls is proved effective by comparisons between models that with and
without the pose prediction module.

本文提出了一种基于单目视频预测人们跌倒的新方法，利用身体关键点的姿势预测模块和跌倒分类器的联合训练，通过对 191 个视频数据集的对比实验展示了预测未来跌倒的有效性。