Frogeye: Perception of the Slightest Tag Motion Lei Yang, Yong Qi, Jianbing Fang, Xuan Ding, Tianci...
If you can't read please download the document
Frogeye: Perception of the Slightest Tag Motion Lei Yang, Yong Qi, Jianbing Fang, Xuan Ding, Tianci Liu, Mo Li Tsinghua University, Xi’an Jiaotong University
Frogeye: Perception of the Slightest Tag Motion Lei Yang, Yong
Qi, Jianbing Fang, Xuan Ding, Tianci Liu, Mo Li Tsinghua
University, Xian Jiaotong University [email protected] 2014-5-2
INFOCOM
Slide 2
Background RFID technology www.barcoding.com www.datasoft.se
www.eff.org www.kennedygrp.com www.forrester.com
TAGSREADERApplications
Slide 3
RFID Overview Portal Conveyor/Assembly line Access control
Livestock Payment devices Logistics Passport Automobile
immobilizers 55
Slide 4
MOTIVATION SECURING VALUABLE OBJECTS The most common solution
is to equip artifacts with various security sensors, such as
displacement sensor, tension sensor, vibration sensors and so on.
As long as the artifacts are moved, alert is reported. These
sensors are very expensive and difficult to be deployed. Camera
surveillance is another attractive option Suffer from dead corners
and dependence of the light. The most common solution is to equip
artifacts with various security sensors, such as displacement
sensor, tension sensor, vibration sensors and so on. As long as the
artifacts are moved, alert is reported. These sensors are very
expensive and difficult to be deployed. Camera surveillance is
another attractive option Suffer from dead corners and dependence
of the light. The Art of Securing Pricelessness
Slide 5
MOTIVATION MINING CONSUMERS BEHAVIOR What are the really
popular products? In an effort to help supermarkets understand
their consumers shopping behaviors, a large number of data mining
techniques have been studied. However, those technique are confined
to the purchased data. In most of time, the consumer takes their
interested goods off the shelf for details but does not purchase
them finally due to price. RFID technology offers an opportunity to
collect these behaviors.
Slide 6
How to perceive the tags motion? At first glance, there is no
any connection between the above two scenarios. Actually, both of
them focus on the surveillance of tag motions: The first needs an
alert when valuable objects are moved; the second requires behavior
records when the products are taken off the shelf. Our goal is to
perceive the tags motion to determine whether the object is moved.
Our approach is not for localization
Slide 7
Opportunity Being hypersensitive
Slide 8
Challenge The Weak Stability Observation 2: The result is not
as stable as expected, because the value occupies several units
even when the tag remains in a same distance. We call this
phenomenon weak stability. Observation 2: The result is not as
stable as expected, because the value occupies several units even
when the tag remains in a same distance. We call this phenomenon
weak stability.
Slide 9
Which causes the weak stability Which causes the weak stability
? Thermal vibration: The electronic components thermal noise brings
strength changes. Interference: when the strength is interfered,
its changes are as significant as when the tag is moved. It is easy
to mistakenly consider a stationary object moved.
Slide 10
Modeling the Thermal Vibration Gaussian Model : We believe this
model is reasonable because a lot of natured phenomena follows the
Gaussian distribution, especially thermal noise from internal
electronic components, which mainly contribute the vibration.
Slide 11
Modeling the Interference This phenomenon is mainly explained
by the multipath effect. There exist several paths for the
backscattered signal propagating from tag to reader. The signal
strength propagating through different paths varies a lot due to
the path length. When the interference object gets close to the
tag, it may block some propagation paths and leads to the
propagation jumping among the multiple paths, resulting in the
strength transmission from one level to another.
Slide 12
Modeling the Interference From a long-term perspective, the
strength exhibits multimodal characteristics where the distribution
is likely composed of multiple Gaussian models.
Slide 13
Basic Idea Our basic idea is to detect the significant changes
of the backscattered signal for perception of tag motion. There is
a high probability that the tag moved when its strength changed
significantly. We find our problem is very similar to the
foreground detection in computer vision, which is to segment the
foreground pixels that significantly differ between the last image
of sequence and the previous images.
Slide 14
Workflow
Slide 15
Preprocessing
Slide 16
Strength Image Construction In the image, each row is uniquely
mapped to a same tag. The mapping fashion between the tags and rows
is arbitrary as long as their mapping remains constant during the
processing. Each column represents a read cycle. The whole image
contains a total of m columns. Formally, given a strength image,
the element x_ij represents a read strength from the tag i
collected in the j^th read cycle of the frame. In the image, each
row is uniquely mapped to a same tag. The mapping fashion between
the tags and rows is arbitrary as long as their mapping remains
constant during the processing. Each column represents a read
cycle. The whole image contains a total of m columns. Formally,
given a strength image, the element x_ij represents a read strength
from the tag i collected in the j^th read cycle of the frame.
Slide 17
Why we convert the strength flow to a visual image? No any
connections between them??
Slide 18
RATIONALE BEHIND Optical System RFID System
Slide 19
MOTION DETECTION IN COMPUTER VISION Frame Differencing The
result is interesting and inspiring
Slide 20
MoG based Foreground Detection Background learning Background
detecting frames The details can be referred to the paper.
Slide 21
Motion Refining collateral motion
Slide 22
Implementation & Evaluation We deploy one reader and 100
tags our noisy office room to evaluate the false positives. We
attach tags on a toy train to measure the false negatives. The
train moves along an oval track in a constant speed.
Slide 23
Evaluation the accuracy is up to 92.34% while the false
positive is suppressed under 0.5%.
Slide 24
Sensitivity The average Minimum Perception displacement equals
6cm.
Slide 25
Evaluation
Slide 26
Slide 27
Conclusion In this paper, our major contributions are
summarized as follows: We conduct statistical analysis of strength
collected in a real-life office, showing that the strength are
hypersensitive to tags positions, but suffers from weak stability
where the strength values are highly clustered in a small range due
to thermal noise, and enhanced or weakened due to multi-path
effect. We present a MOG method to characterize the weak stability.
We propose Frogeye, to perceive the sight of the tag motion. This
approach takes a snapshot of tags positions through their
backscattered strength very several read cycles, producing a
sequence of strength frames. We implement the system using pure
COTS RFID devices and evaluate it at various parameter
choices.