  We propose a new method for anomaly detection of human actions. Our method
works directly on human pose graphs that can be computed from an input video
sequence. This makes the analysis independent of nuisance parameters such as
viewpoint or illumination. We map these graphs to a latent space and cluster
them. Each action is then represented by its soft-assignment to each of the
clusters. This gives a kind of "bag of words" representation to the data, where
every action is represented by its similarity to a group of base action-words.
Then, we use a Dirichlet process based mixture, that is useful for handling
proportional data such as our soft-assignment vectors, to determine if an
action is normal or not.
  We evaluate our method on two types of data sets. The first is a fine-grained
anomaly detection data set (e.g. ShanghaiTech) where we wish to detect unusual
variations of some action. The second is a coarse-grained anomaly detection
data set (e.g., a Kinetics-based data set) where few actions are considered
normal, and every other action should be considered abnormal.
  Extensive experiments on the benchmarks show that our method performs
considerably better than other state of the art methods.
