  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 people over
time. We incorporated a latent feature encoder network which enables the
diffusion process to operate within a high-dimensional latent space to allow
for the extraction and spatial-temporal refinement of such rich features as
person appearance, motion, location, identity, and contextual information.
Extensive experiments demonstrate the effectiveness of LDTrack over other
state-of-the-art tracking methods in cluttered and crowded human-centered
environments under intraclass variations. Namely, the results show our method
outperforms existing deep learning robotic people tracking methods in both
tracking accuracy and tracking precision with statistical significance.
