LDTrack: Dynamic People Tracking by Service Robots using Diffusion Models

arXiv:2402.08774v2 Announce Type: replace
Abstract: Tracking of dynamic people in cluttered and crowded human-centered environments is a challenging robotics problem due to the presence of intraclass variations including occlusions, pose deformations, and lighting variations. This paper introduces a novel deep learning architecture, using conditional latent diffusion models, the Latent Diffusion Track (LDTrack), for tracking multiple dynamic people under intraclass variations. By uniquely utilizing conditional latent diffusion models to capture temporal person embeddings, our architecture can adapt to appearance changes of …

abstract architecture arxiv cs.cv cs.ro deep learning diffusion diffusion models dynamic environments human latent diffusion models lighting novel paper people robotics robots service tracking type