hello everyone my name is trung huidwan
it's my honor today to present our work
full dimension relative positioning for
rfid-enabled
self-checkout services this is a joint
work of qinghua university and beijing
institute of technology
the concept of checkout free or
self-checkout shopping becomes popular
recently it enhances consumer's shopping
experience when compared against the
traditional manual checkout counter
which is time consuming for consumers to
wait in line and also incurs large labor
costs for capturing
although many checkout free options have
been explored for example self scanning
items with the checkout machine or more
complex sensor or camera based solutions
however such solutions are either
inconvenient or require large deployment
and hardware costs
in this work we turn our attention to
the rfid technology we propose a
lightweight and efficient self-checkout
schema with purely commercial off the
shelf devices
as shown in the right figure a conveyor
belt is deployed
moving at a uniform speed to propel the
shopping carts of users we allow
multiple users to check out
simultaneously so there may be multiple
cards on the belt
the key problem here is to determine
which card each item belongs to
we attach a few anchor tags on the
fourth surrounding edges of each card
and the tag attached on each item is
called item tab
then the checkout problem is converted
to relative positioning among item tags
and anchor tags without knowing their
absolute positions
define x dimension as the moving
direction of conveyor
when attack moves its distance to the
reader increases at first until the
reader is right above the tag then
decreases
accordingly the text phase theta will
also first descend to a valley and then
go up like showing in the right figure
on the right
the bottom of the valley in the curve
exactly occurs at the time when the
reader is closest to the tag therefore
we can infer the order of text according
to the above time
then to determine the order of text
along y dimension we find one attack has
a smaller y coordinate its phase prop
file will also show a faster changing
rate on the whole
we propose to integrate the phase
changing rate over a chosen time window
and exploits the integrated value which
we also call the overall phase changing
rate as a metric for ordering tax
as we know in real in-depth scenarios
tax phase is easily affected by
surrounding noise to further increase
the final accuracy we propose to employ
two antennas placed at both sides of the
conveyor
then given two tags j and k if tech j is
closer to antenna one than tag k we can
easily infer that take j is farther from
antenna two than tag k
so here the relative phase change rate
of tag j would be larger than that of
tag k
besides the difference of attack's phase
changing rates between two antennas
would be enlarged compared against the
case when only one antenna is adopted
remember our final goal is to determine
which card an item belongs to
so for each item we first find all the
candidate cards according to the
ordinary result on x dimension then we
compute the score p to measure the
probability that every candidate indeed
carries the item according to the
ordinary result on y dimension
finally by traversing all the candidates
the target item is supposed to be
carried by the card with the highest
probability
that's the whole workflow of our
approach
we implement a prototype of our system
one commercial affair and two antennas
are employed to collect the phase
information of tags a mobile robot is
used to simulate a conveyor belt
we first evaluate the tag ordering
accuracy and compare our work with
related literature
the mean accuracy can reach 93 percent
on x dimension and 89 are wide damage
besides to evaluate the robustness of
our system in different settings we
change the number of cards the distance
between cards and also experiment on
different models of tags
results show that the overall matching
accuracy of items and cards can reach 95
percent on average in various settings
now in summary we present a lightweight
and efficient self-checkout system based
on commercial rfid products
we design a full dimension relative
positioning schema and propose an item
classifying solution based on
probabilistic modeling we implement a
prototype system and evaluate results
show that the final classified accuracy
can reach as high as 95 percent
that's all of my talk for more details
please refer to our paper thanks for
your attention