Frogeye: Perception of the Slightest Tag Motion

1. Frogeye: Perception of the Slightest Tag Motion Lei Yang, Yong Qi, JianbingFang,XuanDing,TianciLiu,MoLi Tsinghua University, Xi’an Jiaotong University young@tagsys.org 2014-5-2 INFOCOM

2. Background – RFID technology www.datasoft.se www.eff.org www.forrester.com www.kennedygrp.com www.barcoding.com TAGS READER Applications

3. RFID Overview 5¢ Conveyor/Assembly line • Portal • Access control • Livestock Passport Automobile immobilizers Logistics Payment devices

4. MOTIVATION – SECURING VALUABLE OBJECTS The Art of Securing Pricelessness • Themostcommonsolutionistoequipartifactswithvarioussecuritysensors,suchasdisplacementsensor,tensionsensor, vibrationsensorsandsoon.Aslongastheartifactsaremoved,alertisreported. • Thesesensorsareveryexpensiveanddifficulttobedeployed. • Camerasurveillanceisanotherattractiveoption • Sufferfromdeadcornersanddependenceofthelight.

5. MOTIVATION – MINING CONSUMER’S BEHAVIOR What are the really popular products? • In an effort to help supermarkets understand their consumer’s 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.

6. 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 tag’s motion to determine whether the object is moved. How to perceive the tag’s motion? Ourapproachisnotforlocalization

7. Opportunity – Being hypersensitive Observation 1: the strength is indeed hypersensitive to the distance. The strength difference is very noticeable even if the two positions are very close ().

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.

9. Which causes the weak stability? Thermal vibration: The electronic component’s 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.

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.

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.

12. Modeling the Interference From a long-term perspective, the strength exhibits multimodal characteristics where the distribution is likely composed of multiple Gaussian models.

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.

14. Workflow

15. Preprocessing

16. Strength Image Construction Intheimage,eachrowisuniquelymappedtoasametag.Themappingfashionbetweenthetagsandrowsisarbitraryaslongastheirmappingremainsconstantduringtheprocessing.Eachcolumnrepresentsareadcycle.Thewholeimagecontainsatotalofmcolumns.Formally,givenastrengthimage, theelementx_ijrepresentsareadstrengthfromthetagicollectedinthej^threadcycleoftheframe.

17. Why we convertthe strength flow to a visual image? No any connections between them??

18. RATIONALE BEHIND RFID System Optical System

19. MoG based Foreground Detection Backgroundlearning frames Backgrounddetecting The details can be referred to the paper.

20. 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.

21. Evaluation the accuracy is up to 92.34% while the false positive is suppressed under 0.5%.

22. Sensitivity The average Minimum Perception displacement equals 6cm.

23. Evaluation

24. Evaluation

25. Conclusion • Inthispaper,ourmajorcontributionsaresummarizedasfollows: • Weconductstatisticalanalysisofstrengthcollectedinareal-lifeoffice,showingthatthestrengtharehypersensitivetotags’positions,butsuffersfromweakstabilitywherethestrengthvaluesarehighlyclusteredinasmallrangeduetothermalnoise,andenhancedorweakenedduetomulti-patheffect.WepresentaMOGmethodtocharacterizetheweakstability. • WeproposeFrogeye,toperceivethesightofthetagmotion.Thisapproachtakesasnapshotoftags’positionsthroughtheirbackscatteredstrengthveryseveralreadcycles,producingasequenceofstrengthframes. • WeimplementthesystemusingpureCOTSRFIDdevicesandevaluateitatvariousparameterchoices.

26. Thanks !Q&A