LDTrack：使用扩散模型的服务机器人动态人员跟踪
LDTrack: Dynamic People Tracking by Service Robots using Diffusion Models
AI总结：
针对机器人跟踪动态人员的难题，提出基于条件潜在扩散模型的LDTrack架构，通过时空特征提取与细化，在拥挤环境中显著提升跟踪精度与准确性。
摘要：
在杂乱且拥挤的以人为中心的环境中跟踪动态人员是一个具有挑战性的机器人问题，因为存在类内变化，包括遮挡、姿态变形和光照变化。本文介绍了一种新颖的深度学习架构，使用条件潜在扩散模型，即潜在扩散跟踪（LDTrack），用于在类内变化下跟踪多个动态人员。通过独特地利用条件潜在扩散模型来捕获时间人员嵌入，我们的架构能够适应人员随时间的外观变化。我们整合了一个潜在特征编码器网络，使扩散过程能够在高维潜在空间内运行，从而能够提取和时空细化诸如人员外观、运动、位置、身份和上下文信息等丰富特征。大量实验证明了LDTrack在杂乱且拥挤的以人为中心的环境中，在类内变化下优于其他最先进的跟踪方法。即，结果表明，我们的方法在跟踪准确性和跟踪精度方面均以统计显著性优于现有的深度学习机器人人员跟踪方法。此外，针对城市环境中的最先进方法进行了全面的多目标跟踪比较研究，证明了LDTrack的泛化能力。进行了消融研究以验证LDTrack的设计选择。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
