In this paper, we propose NeuralTBD, utilizing the capability of deep models and advancement of Tracking-Before-Detect (TBD) methodology to achieve accurate ...
The second challenge lies in the absence of a public dataset for unconstrained indoor human tracking. The main reason is that RF signal is not human readable ...
Learning-Based Tracking-before-Detect for RF-Based Unconstrained Indoor Human Tracking. IJCAI, 2024; Qian Liang, Yan Chen, Yang Hu, Continual Learning for ...
PRISM: Pre-training RF Signals in Sparsity-aware Masked Autoencoders ... Learning-Based Tracking-before-Detect for RF-Based Unconstrained Indoor Human Tracking.
Machine Learning -> ML: Theory of deep learning. 3891. Learning-Based Tracking-before-Detect for RF-Based Unconstrained Indoor Human Tracking. Zhi Wu, Dongheng ...
Zhi Wu's 21 research works with 165 citations, including: Learning-Based Tracking-before-Detect for RF-Based Unconstrained Indoor Human Tracking.
IEEE Transactions on Mobile Computing (TMC), 2024. Learning-based Tracking-Before-Detect for RF-based Unconstrained Indoor Human Tracking Zhi Wu, Dongheng ...
To evaluate NeuralTBD, we collect an RF-based tracking dataset in unconstrained scenarios, which encompasses 4 million annotated radar frames with up to 19 ...
[PDF] RFMask: A Simple Baseline for Human Silhouette Segmentation ...
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Experimental results show that the proposed RFMask framework can achieve impressive human silhouette segmentation even under the challenging scenarios where ...
In particular, our work mainly focuses on passive speed estimation, motion detection, sleep monitoring, and indoor tracking for wireless sensing. In this ...