25,928 research outputs found
Human Sensing via Passive Spectrum Monitoring
Human sensing is significantly improving our lifestyle in many fields such as
elderly healthcare and public safety. Research has demonstrated that human
activity can alter the passive radio frequency (PRF) spectrum, which represents
the passive reception of RF signals in the surrounding environment without
actively transmitting a target signal. This paper proposes a novel passive
human sensing method that utilizes PRF spectrum alteration as a biometrics
modality for human authentication, localization, and activity recognition. The
proposed method uses software-defined radio (SDR) technology to acquire the PRF
in the frequency band sensitive to human signature. Additionally, the PRF
spectrum signatures are classified and regressed by five machine learning (ML)
algorithms based on different human sensing tasks. The proposed Sensing Humans
among Passive Radio Frequency (SHAPR) method was tested in several environments
and scenarios, including a laboratory, a living room, a classroom, and a
vehicle, to verify its extensiveness. The experimental results show that the
SHAPR method achieved more than 95% accuracy in the four scenarios for the
three human sensing tasks, with a localization error of less than 0.8 m. These
results indicate that the SHAPR technique can be considered a new human
signature modality with high accuracy, robustness, and general applicability
A PatchMatch-based Dense-field Algorithm for Video Copy-Move Detection and Localization
We propose a new algorithm for the reliable detection and localization of
video copy-move forgeries. Discovering well crafted video copy-moves may be
very difficult, especially when some uniform background is copied to occlude
foreground objects. To reliably detect both additive and occlusive copy-moves
we use a dense-field approach, with invariant features that guarantee
robustness to several post-processing operations. To limit complexity, a
suitable video-oriented version of PatchMatch is used, with a multiresolution
search strategy, and a focus on volumes of interest. Performance assessment
relies on a new dataset, designed ad hoc, with realistic copy-moves and a wide
variety of challenging situations. Experimental results show the proposed
method to detect and localize video copy-moves with good accuracy even in
adverse conditions
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