Computer Science > Computer Vision and Pattern Recognition
[Submitted on 4 Jan 2020 (v1), last revised 16 Feb 2020 (this version, v2)]
Title:Human Action Recognition and Assessment via Deep Neural Network Self-Organization
View PDFAbstract:The robust recognition and assessment of human actions are crucial in human-robot interaction (HRI) domains. While state-of-the-art models of action perception show remarkable results in large-scale action datasets, they mostly lack the flexibility, robustness, and scalability needed to operate in natural HRI scenarios which require the continuous acquisition of sensory information as well as the classification or assessment of human body patterns in real time. In this chapter, I introduce a set of hierarchical models for the learning and recognition of actions from depth maps and RGB images through the use of neural network self-organization. A particularity of these models is the use of growing self-organizing networks that quickly adapt to non-stationary distributions and implement dedicated mechanisms for continual learning from temporally correlated input.
Submission history
From: German I. Parisi [view email][v1] Sat, 4 Jan 2020 15:58:06 UTC (2,422 KB)
[v2] Sun, 16 Feb 2020 16:56:09 UTC (2,454 KB)
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