LDTrack: 拡散モデルを用いたサービスロボットによる動的人物追跡
LDTrack: Dynamic People Tracking by Service Robots using Diffusion Models
条件付き潜在拡散モデルを活用し、遮蔽や姿勢変化などの見た目の変動に頑健な複数人物追跡手法を提案。混雑環境での追跡精度と精度が既存手法を統計的に有意に上回ることを示した。
著者: Angus Fung, Beno Benhabib, Goldie Nejat
分類: cs.CV, cs.RO
原文アブストラクト
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. Additionally, a comprehensive multi-object tracking comparison study was performed against the state-of-the-art methods in urban environments, demonstrating the generalizability of LDTrack. An ablation study was performed to validate the design choices of LDTrack.
