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  1. 1. Ferret: RFID Localization for Pervasive Multimedia Xiaotao Liu, Mark Corner, Prashant Shenoy University of Massachusetts, Amherst
  2. 2. Scenario: I’ve Lost my Keys <ul><li>People frequently misplace common items </li></ul><ul><ul><li>books, keys, tools, clothing, etc. </li></ul></ul><ul><ul><li>difficult due to the sheer scale: we interact with >1000s of items </li></ul></ul><ul><li>Need a system to find objects quickly and efficiently </li></ul><ul><ul><li>then tell the user where the object is </li></ul></ul>
  3. 3. Problems <ul><li>Tracking objects can be broken into sub-problems </li></ul><ul><ul><li>Locate: find position, perhaps not exact, but a general idea </li></ul></ul><ul><ul><li>Store: keep object locations in a convenient place </li></ul></ul><ul><ul><li>Update: when objects move, need to change store </li></ul></ul><ul><ul><li>Display: Present locations to user in a helpful way </li></ul></ul>
  4. 4. Solution: Ferret <ul><li>Provides a real-time augmented reality service </li></ul><ul><ul><li>locates, stores, updates, and displays object locations </li></ul></ul><ul><ul><li>intended for nomadic objects not mobile ones </li></ul></ul><ul><li>Leverage passive RFID, multimedia, and location systems </li></ul><ul><ul><li>passive RFID: inexpensive, scalable, maintenance-free </li></ul></ul><ul><ul><li>multimedia systems: provide convenient display and storage </li></ul></ul><ul><ul><li>location systems: bootstrap process of finding locations </li></ul></ul><ul><li>Goal is to pack all functions into a hand held device </li></ul><ul><ul><li>including RFID detection, storage, and display </li></ul></ul><ul><ul><li>a combination of video camera and RFID reader </li></ul></ul>
  5. 5. Outline <ul><li>Motivation and Applications </li></ul><ul><li>Overview of Use </li></ul><ul><li>Design of Ferret </li></ul><ul><ul><li>Sensor model </li></ul></ul><ul><ul><li>Offline location algorithm </li></ul></ul><ul><ul><li>Online location algorithm </li></ul></ul><ul><ul><li>Display </li></ul></ul><ul><ul><li>In paper: Storage, Update for nomadic objects </li></ul></ul><ul><li>Prototype implementation </li></ul><ul><li>Experiments </li></ul><ul><ul><li>Speed and accuracy </li></ul></ul><ul><ul><li>Robustness to different movement patterns </li></ul></ul><ul><li>Related Work </li></ul><ul><li>Conclusions </li></ul>
  6. 6. Overview of Operation <ul><li>User selects some object(s) that she is looking for </li></ul><ul><li>She wanders around a room, or building, holding Ferret system </li></ul><ul><li>During this process, the reader scans for nearby RFID tags </li></ul><ul><li>Ferret detects the RFID tag of interest, localizes tag </li></ul><ul><li>It then displays an outline of where the object is on the screen </li></ul><ul><ul><li>willing to settle for a probable region of where the object is </li></ul></ul><ul><ul><li>depend on human skill to find the exact location </li></ul></ul><ul><ul><li>refine region as system runs </li></ul></ul><ul><ul><li>present improved results in real-time </li></ul></ul>
  7. 7. RFID Localization <ul><li>Passive RFID tags are not self-locating </li></ul><ul><li>Instead we depend on the handheld to locate tags </li></ul><ul><li>Passive RFID tags have significant error rates </li></ul><ul><ul><li>false negatives are frequent </li></ul></ul><ul><ul><li>false positives due to reflections </li></ul></ul><ul><li>Locate using probabilistic model </li></ul><ul><ul><li>inspired by [H ä hnel et. al] </li></ul></ul>RFID reader 2. use RF energy to charge up 1. energy 3. id
  8. 8. Bayesian Probability Model <ul><li>Goal: p ( x | D 1:n ): Probability of tag at x given readings </li></ul><ul><li>Initially, without readings, p ( x | D 0 ) is uniformly distributed </li></ul><ul><li>Assume we have p ( x | D 1:n ) </li></ul><ul><li>Positive reading </li></ul><ul><ul><li>p ( D n+1 =True| x) </li></ul></ul><ul><li>Bayes’ rule p ( x | D 1:n+1 ) = α p ( x | D 1:n ) p ( D n+1 | x ) </li></ul><ul><ul><li>α – normalization factor </li></ul></ul><ul><li>Similarly, for negative readings </li></ul><ul><ul><li>p ( D n+1 =False| x ) = 1 - p ( D n+1 =True| x ) </li></ul></ul>
  9. 9. Tag Detection Probability <ul><li>Manually measure probability of detecting tag (positive reading) </li></ul><ul><ul><li>p ( D =True| x ) x – tag’s position </li></ul></ul>
  10. 10. Ferret Localization Algorithm (+ reading) <ul><li>Multiple readings come from user mobility, previous, or shared readings </li></ul>
  11. 11. Ferret Localization Algorithm (- reading) Repeated intersection of positive and negative readings
  12. 12. Offline Algorithm Complexity <ul><li>We refer to the previous algorithm as the “offline” algorithm </li></ul><ul><li>Each + or - reading Ferret performs O(n^3) operations </li></ul><ul><ul><li>n is the number of sample points </li></ul></ul><ul><ul><li>it must rotate, translate the RFID sensor model </li></ul></ul><ul><ul><li>multiply each sample point against every other sample point </li></ul></ul><ul><ul><li>must do this for each object! </li></ul></ul><ul><li>Computational requirements at least 0.7s on a laptop </li></ul><ul><ul><li>reader is producing at least 4 readings per second </li></ul></ul><ul><ul><li>some readings include multiple objects </li></ul></ul><ul><li>Algorithm most useful for back-annotating video </li></ul>
  13. 13. Online Algorithm <ul><li>To address real-time concerns use an “online” algorithm </li></ul><ul><ul><li>instead of intersecting all interior points, just find convex intersection </li></ul></ul><ul><ul><li>only uses positive readings, not negative ones (keeps shape convex!) </li></ul></ul><ul><li>Complexity reduced to O(n^2) or 6ms per reading </li></ul>
  14. 14. Display <ul><li>Each RFID location is a 3-D shape </li></ul><ul><li>To display we simply project this 3-D shape onto a 2-D screen </li></ul>
  15. 15. Ferret Prototype <ul><li>ThingMagic Mercury4 RFID reader </li></ul><ul><ul><li>30dBm (1 Watt), monostatic circular antenna </li></ul></ul><ul><li>Alien Technology “M” RFID Tag </li></ul><ul><ul><li>EPC Class 1, 915 MHz </li></ul></ul><ul><li>Sony Motion Eye web-camera </li></ul><ul><ul><li>320x240 at 12fps </li></ul></ul><ul><li>Cricket Ultrasound 3-D locationing system </li></ul><ul><ul><li>global location not necessary, but need relative locations at least </li></ul></ul><ul><li>Sparton SP3003 Digital Compass </li></ul><ul><ul><li>Pan, tilt, and roll </li></ul></ul><ul><li>Software </li></ul><ul><ul><li>translate between coordinate systems, rotate, and display </li></ul></ul>
  16. 16. Ferret Prototype Cricket locationing sensor Compass RFID antenna ThingMagic RFID reader Built-in Camera
  17. 17. Evaluation <ul><li>Evaluation metrics: </li></ul><ul><ul><li>Size of location region for many objects </li></ul></ul><ul><ul><li>Speed of localization for a particular object </li></ul></ul><ul><ul><li>Robustness of localization to mobility patterns </li></ul></ul><ul><li>Evaluation setup for many objects: </li></ul><ul><ul><li>Place 30+ objects with passive tags around the room </li></ul></ul><ul><ul><li>Move Ferret system around the room by human for 20 minutes </li></ul></ul><ul><ul><li>CDF of localization over 30 objects </li></ul></ul><ul><li>Evaluation setup for single object: </li></ul><ul><ul><li>Place single object in room with passive tag </li></ul></ul><ul><ul><li>Move Ferret system in and out of view randomly and using a specific pattern </li></ul></ul><ul><ul><li>Size of localization after some amount of time </li></ul></ul>
  18. 18. Online Vs Offline (CDF-30 Objects) Offline algorithm outperforms online, but most objects localized to 0.2 m^3
  19. 19. Refinement: Relative Volume (1 Object) Volume size drops down 100 times to 0.02m3 in 2 mins When starting with previous readings, localization is faster
  20. 20. Refinement: Relative Projection Area Final projection area decreases 33 times in 2 mins to a 54 pixel diameter circle
  21. 21. Different Movement Patterns <ul><li>Circular motion pattern performs the worst: no diversity in views </li></ul><ul><li>Offline algorithm’s advantage comes from negative readings </li></ul><ul><ul><li>so head-on and circular perform similarly </li></ul></ul>1.23 0.026 0.032 Circle 23.63 13.52 1.40 13.33 Offline/ Online 0.0011 0.0017 0.0030 0.0015 offline Volume (m^3) 0.026 0.023 0.0042 0.020 online Volume (m^3) Rotate z-Line Head-on Straight
  22. 22. Related Work <ul><li>Grown out of our work on Sensor Enhanced Video Annotation </li></ul><ul><ul><li>SEVA ACM Multimedia 2005 (Best Paper Award) </li></ul></ul><ul><ul><li>Used active sensors for location </li></ul></ul><ul><li>RFID Localization inspired by techniques from [H ä hnel et. al] </li></ul><ul><ul><li>2-D sensor model, application of Bayes rule positive readings </li></ul></ul><ul><ul><li>we add 3-D model, negative readings, and online technique </li></ul></ul><ul><ul><li>focuses on SLAM/localizing reader, we focus on reverse </li></ul></ul><ul><li>LANDMARC and SpotON RFID locationing </li></ul><ul><ul><li>active RFID and signal strength </li></ul></ul>
  23. 23. Conclusions <ul><li>Ferret: a scalable, RFID-based, augmented reality system </li></ul><ul><ul><li>localize objects augmented with passive RFID tags </li></ul></ul><ul><ul><li>display probable location regions to a user in real-time </li></ul></ul><ul><li>Uses two algorithms: online and offline </li></ul><ul><ul><li>both are accurate and efficient (localizes objects to 0.2m^3 in minutes) </li></ul></ul><ul><ul><li>robust to a variety of user mobility patterns </li></ul></ul><ul><li>Ferret lays the ground work for other augmented reality applications </li></ul>
  24. 24. Ferret: RFID Localization for Pervasive Multimedia Xiaotao Liu, Mark Corner, Prashant Shenoy University of Massachusetts, Amherst
  25. 26. Location Storage <ul><li>Locations (3-Dimensional probability maps) </li></ul><ul><li>Storage on reader </li></ul><ul><ul><li>simple to implement, but must acquire readings as it goes </li></ul></ul><ul><li>Database </li></ul><ul><ul><li>any Ferret readers can take advantage of prior knowledge </li></ul></ul><ul><ul><li>also permits offline searching, but privacy/authorization concerns </li></ul></ul><ul><li>Storage on writable tags </li></ul><ul><ul><li>tags self-locating and provide locations to non-Ferret systems </li></ul></ul>
  26. 27. What if objects move? <ul><li>Nomadic objects may have moved since previous readings </li></ul><ul><ul><li>when online algorithm detects empty intersection, reset </li></ul></ul><ul><ul><li>offline algorithm more complex, uses a probability threshold </li></ul></ul>
  27. 28. Ferret Software Architecture Bayesian Locationing Module Device Drivers for Cricket and Compass RFID Module (operate RFID reader) Ferret System Video Recording Visualization Module (modified from FFmpeg) via TCP, Use SQL-like language <ul><ul><li>Deal with large amount of data, </li></ul></ul><ul><ul><li>Optimized for real-time usage </li></ul></ul>Use optics model Intercept original display function Fuse video, tag’s location together Compute projection of location estimates Display projection boundary
  28. 29. [H ä hnel et. al] <ul><li>“ To each of the randomly chosen potential positions we </li></ul><ul><li>assign a numerical value storing the posterior probability </li></ul><ul><li>p(x | z1:t) that this position corresponds to the true pose of </li></ul><ul><li>the tag. Whenever the robot detects a tag, the posterior is </li></ul><ul><li>updated according to Equation (1) and using the sensor model </li></ul><ul><li>described in the previous section.” </li></ul><ul><li>In this paper we analyze whether recent Radio Frequency Identification (RFID) technology can be used to improve the localization of mobile robots and persons in their environment. </li></ul>

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