232 research outputs found
On multipath link characterization and adaptation for device-free human detection
Abstract—Wireless-based device-free human sensing has raised increasing research interest and stimulated a range of novel location-based services and human-computer interaction appli-cations for recreation, asset security and elderly care. A primary functionality of these applications is to first detect the presence of humans before extracting higher-level contexts such as physical coordinates, body gestures, or even daily activities. In the presence of dense multipath propagation, however, it is non-trivial to even reliably identify the presence of humans. The multipath effect can invalidate simplified propagation models and distort received signal signatures, thus deteriorating detection rates and shrinking detection range. In this paper, we characterize the impact of human presence on wireless signals via ray-bouncing models, and propose a measurable metric on commodity WiFi infrastructure as a proxy for detection sensitivity. To achieve higher detection rate and wider sensing coverage in multipath-dense indoor scenarios, we design a lightweight subcarrier and path configuration scheme harnessing frequency diversity and spatial diversity. We prototype our scheme with standard WiFi devices. Evaluations conducted in two typical office environments demonstrate a detection rate of 92.0 % with a false positive of 4.5%, and almost 1x gain in detection range given a minimal detection rate of 90%. I
DN-DETR: Accelerate DETR Training by Introducing Query DeNoising
We present in this paper a novel denoising training method to speedup DETR
(DEtection TRansformer) training and offer a deepened understanding of the slow
convergence issue of DETR-like methods. We show that the slow convergence
results from the instability of bipartite graph matching which causes
inconsistent optimization goals in early training stages. To address this
issue, except for the Hungarian loss, our method additionally feeds
ground-truth bounding boxes with noises into Transformer decoder and trains the
model to reconstruct the original boxes, which effectively reduces the
bipartite graph matching difficulty and leads to a faster convergence. Our
method is universal and can be easily plugged into any DETR-like methods by
adding dozens of lines of code to achieve a remarkable improvement. As a
result, our DN-DETR results in a remarkable improvement (AP) under the
same setting and achieves the best result (AP and with and
epochs of training respectively) among DETR-like methods with ResNet-
backbone. Compared with the baseline under the same setting, DN-DETR achieves
comparable performance with training epochs. Code is available at
\url{https://github.com/FengLi-ust/DN-DETR}.Comment: To appear in CVPR 202
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