A Novel Skeleton-Based Human Activity Discovery Using Particle Swarm Optimization with Gaussian Mutation. (arXiv:2201.05314v4 [cs.CV] UPDATED)

Human activity discovery aims to cluster the activities performed by humans
without any prior information on what defines each activity. Most methods
presented in human activity recognition are supervised, where there are labeled
inputs to train the system. In reality, it is difficult to label activities
data because of its huge volume and the variety of human activities. This paper
proposes an unsupervised framework to perform human activity discovery in 3D
skeleton sequences. First, an approach for data pre-processing is …

arxiv discovery human mutation optimization