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Found 4 papers, 0 papers with code
LDTrack: Dynamic People Tracking by Service Robots using Diffusion Models
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.
Robots Autonomously Detecting People: A Multimodal Deep Contrastive Learning Method Robust to Intraclass Variations
Robotic detection of people in crowded and/or cluttered human-centered environments including hospitals, long-term care, stores and airports is challenging as people can become occluded by other people or objects, and deform due to variations in clothing or pose.
Multimodal Detection of COVID-19 Symptoms using Deep Learning & Probability-based Weighting of Modes
The results show considerable improvements in the detection of COVID-19 using our weighting function when compared to an equal weighting function.
Robots Understanding Contextual Information in Human-Centered Environments using Weakly Supervised Mask Data Distillation
To identify and segment contextual information from complex images obtained in these environments, data-driven methods such as Convolutional Neural Networks (CNNs) are used.
