A Novel Skeleton-Based Human Activity Discovery Technique Using Particle Swarm Optimization with Gaussian Mutation. (arXiv:2201.05314v1 [cs.CV])

Human activity discovery aims to distinguish the activities performed by
humans, without any prior information of 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 data
because of its huge volume and the variety of activities performed by humans.
In this paper, a novel unsupervised approach is proposed to perform human
activity discovery in 3D skeleton sequences. First, important frames …

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