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Cheap, Accurate RFID Tracking of Museum Visitors for Personalised Content Delivery
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Cheap, Accurate RFID Tracking of Museum Visitors for Personalised Content Delivery

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A presentation from Museums and the Web 2009....

A presentation from Museums and the Web 2009.

Timothy Baldwin, University of Melbourne, Australia
Lejoe Thomas Kuriakose, University of Melbourne, Australia

In this project, we explored the deployment of RFID-based technologies to observe visitors’ behaviour in a museum to record what exhibits they visit and when. This data has tremendous potential to enhance museum visits through personalizing information via dynamic interfaces, or profiling the visitor to make recommendations for future activity inside or outside the museum. The outcomes of the project demonstrate the viability of (passive) RFID technologies for museum visitor tracking and provide empirical validation of the near-human tracking accuracy of the system in two different environments.

Session: Location-Aware Services [Technology]

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Cheap, Accurate RFID Tracking of Museum Visitors for Personalised Content Delivery Presentation Transcript

  • 1. Cheap, Accurate RFID Tracking of Museum Visitors for Personalized Content Delivery Timothy Baldwin and Lejoe Thomas Kuriakose
  • 2. 1 RFID Tracking of Museum Visitors for Personalized Content Delivery Kubadji Project Overview • The Kubadji project is the combination of: user modelling language technology in settings that: (a) are informationally rich; (b) are occupied by users with different needs/backgrounds; (c) have expert domain knowledge of user behaviour; and (d) are associated with contexts and user behaviours that are informative in understanding users. MW 2009: 18 April, 2009
  • 3. 2 RFID Tracking of Museum Visitors for Personalized Content Delivery to . . . ? –W hat ’s t ore his is relate em ? ll m Te – does th so far!? story me – The sa the of ore – How M ––W her en ext ? Image: http://www.flickr.com/photos/paullew/2639507829/ MW 2009: 18 April, 2009
  • 4. 3 RFID Tracking of Museum Visitors for Personalized Content Delivery • Initial focus on: virtual simulation of the museum environment modelling based on historical data and incorporating static (web documents, Wikipedia, curator data, physical layout, ...) and dynamic (observation of visitor behaviour, relevance feedback, free-text interaction, ...) information • Collaboration between University of Melbourne, Monash University and Melbourne Museum MW 2009: 18 April, 2009
  • 5. 4 RFID Tracking of Museum Visitors for Personalized Content Delivery – Path prediction – Exhibit recommendation – Prediction of exhibit relatedness – Personalised summaries MW 2009: 18 April, 2009
  • 6. 4 RFID Tracking of Museum Visitors for Personalized Content Delivery – Visitor tracking – Path prediction – Exhibit recommendation – Prediction of exhibit relatedness – Personalised summaries MW 2009: 18 April, 2009
  • 7. 4 RFID Tracking of Museum Visitors for Personalized Content Delivery – Visitor tracking – Path prediction – Exhibit recommendation – Prediction of exhibit relatedness – Personalised summaries MW 2009: 18 April, 2009
  • 8. 5 RFID Tracking of Museum Visitors for Personalized Content Delivery – Visitor tracking – Path prediction – Exhibit recommendation – Prediction of exhibit relatedness – Personalised summaries MW 2009: 18 April, 2009
  • 9. 6 RFID Tracking of Museum Visitors for Personalized Content Delivery Museum Visitor Tracking • Basic problem: track the path of each museum visitor with no intrusion at minimal cost as accurately as possible • Underlying questions: how accurate do we need to be for “useful” personalisation? how do we measure accuracy? MW 2009: 18 April, 2009
  • 10. 7 RFID Tracking of Museum Visitors for Personalized Content Delivery Contributions of this Research • Development of an evaluation framework for “discrete” tracking, e.g. for museum purposes • Comparison of tracking accuracy under different modalities, and finding that proximity-based tracking is a reasonable proxy for full-on gaze-based tracking MW 2009: 18 April, 2009
  • 11. 8 RFID Tracking of Museum Visitors for Personalized Content Delivery Mainstream Tracking Options • Passive RFID • Active RFID • Bluetooth • Wireless LAN • (Indoor) GPS MW 2009: 18 April, 2009
  • 12. 8 RFID Tracking of Museum Visitors for Personalized Content Delivery Mainstream Tracking Options • Passive RFID • Active RFID • Bluetooth • Wireless LAN • (Indoor) GPS MW 2009: 18 April, 2009
  • 13. 9 RFID Tracking of Museum Visitors for Personalized Content Delivery Sensing vs. “Bookmarking” vs. Tracking • Sensing = automatically sensing that a visitor is in the proximity of a given exhibit (and acting accordingly) • Bookmarking = visitor “swipes” a given exhibit (e.g. to get more information post-visit) • Tracking = tracing a given visitor’s path through the museum space MW 2009: 18 April, 2009
  • 14. 10 RFID Tracking of Museum Visitors for Personalized Content Delivery Continuous vs. Discrete Tracking • Ultimately, we are interested in which exhibits a given visitor has visited, when, and for how long; as such, discrete tracking is (probably) sufficient for our purposes Continuous: Discrete: vs. MW 2009: 18 April, 2009
  • 15. 11 RFID Tracking of Museum Visitors for Personalized Content Delivery Spin-off Benefits of Tracking • “Heat mapping” of visitor pathways through the museum • Analysis of exhibit “clusters” • Profiling of different visitor types • Targeted marketing MW 2009: 18 April, 2009
  • 16. 12 RFID Tracking of Museum Visitors for Personalized Content Delivery The Gory Details MW 2009: 18 April, 2009
  • 17. 13 RFID Tracking of Museum Visitors for Personalized Content Delivery Hardware Set-up • Visitors provided with passive RFID tags, housed in a badge holder worn around the neck • Exhibits/exhibit areas instrumented with RFID antennae, connected to individual RFID readers • RFID readers connected to a central server via USB connections • Tracker output takes the form of a sequence of exhibiti, visitorj , timestart, timeend tuples MW 2009: 18 April, 2009
  • 18. 14 RFID Tracking of Museum Visitors for Personalized Content Delivery Man vs. Machine • The basic question we are asking is: how accurate is automatic tracking? • To answer this question, we compare RFID-based tracking to human tracking over a series of visitors to two separate mini-galleries, with the humans alternating between the two “tracking modalities” of: 1. physical proximity-based exhibit “engagement” 2. visitor gaze-based exhibit “engagement” MW 2009: 18 April, 2009
  • 19. 15 RFID Tracking of Museum Visitors for Personalized Content Delivery Tracking Experiment • Two separate mini-galleries, each with three exhibits: • Two human trackers (same tracking modality) • 15 visitors in total, one at a time MW 2009: 18 April, 2009
  • 20. 16 RFID Tracking of Museum Visitors for Personalized Content Delivery GeckoTracker • The human trackers used the GeckoTracker tracking software to record where a given visitor went • (Same software used for large-scale full-visit tracking experiment through Melbourne Museum) MW 2009: 18 April, 2009
  • 21. 17 RFID Tracking of Museum Visitors for Personalized Content Delivery Evaluation • Basic methodology, given an evaluation metric: evaluate the “agreement” between the two human trackers [gold-standard] evaluate the “agreement” between the RFID output and the intersection of the two human trackers compare the two MW 2009: 18 April, 2009
  • 22. 18 RFID Tracking of Museum Visitors for Personalized Content Delivery Evaluation Metric 1: Edit Distance • Intuition: how many “tweaks” to the sequence of exhibits returned by method A need to be made to transform it into the sequence for method B? [ignore time spent at exhibits] d( E1, E3, E1 , E2, E1 ) = 2 d( E1, E3, E1 , E3, E1 ) = 1 • Use edit distance to calculate the distance between a given pair of sequences MW 2009: 18 April, 2009
  • 23. 19 RFID Tracking of Museum Visitors for Personalized Content Delivery Evaluation Metric 2: Paired t-test • Intuition: what is the statistical similarity in the timed sequence of exhibits returned by method A relative to that for method B? t( E1(0 : 10, 0 : 25), ... , E2(0 : 25, 0 : 37), ... ) = 0.7 t( E1(0 : 10, 0 : 25), ... , E1(0 : 10, 0 : 27), ... ) = 0.1 • Normalise the times, and calculate using the two-tailed paired t-test MW 2009: 18 April, 2009
  • 24. 20 RFID Tracking of Museum Visitors for Personalized Content Delivery Results: Human vs. Machine (Gallery 1) Tracker Edit Paired Modality Pairing Distance t-test Human vs. Human 0.50 0.41 Distance Human vs. machine 0.75 0.71 Human vs. Human 0.75 0.43 Gaze Human vs. machine 1.00 0.76 MW 2009: 18 April, 2009
  • 25. 21 RFID Tracking of Museum Visitors for Personalized Content Delivery Results: Human vs. Machine (Gallery 2) Tracker Edit Paired Modality Pairing Distance t-test Human vs. Human 1.25 0.17 Distance Human vs. machine 2.25 0.36 Human vs. Human 1.25 0.30 Gaze Human vs. machine 1.50 0.37 MW 2009: 18 April, 2009
  • 26. 22 RFID Tracking of Museum Visitors for Personalized Content Delivery Numbers, Numbers Everywhere ... • RFID-based tracker always performs worse than human performance • Relative human vs. machine agreement is almost unchanged between the distance and gaze modalities • Open-plan galleries are more of a challenge for both humans and machines MW 2009: 18 April, 2009
  • 27. 23 RFID Tracking of Museum Visitors for Personalized Content Delivery Error Analysis • False negatives the primary cause of errors for the RFID- based tracker • Signal dropout also contributed MW 2009: 18 April, 2009
  • 28. 24 RFID Tracking of Museum Visitors for Personalized Content Delivery Summary • Promising results for RFID-based tracking • Proposal of a series of evaluation metrics for tracking evaluation • Solid platform to do all the sexy research ... MW 2009: 18 April, 2009