Vergleich derVisualisierungsmöglichkeiteneiner Real-World Search-EngineRobin Boldt, Marcus Eisele, Taha Yalçin

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Introduction Related Work System User Study Results Conclusion Future Work

Introduction

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Related Work

webstickers (Ljungstrand et al.)Antonius (Funk et al.)

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Related Work

webstickers (Ljungstrand et al.)

Halo (Patrick Baudisch)

Searchlight (Butz et al.)

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Two Main Components Indexing Engine (extended version of Antonius, powered by

OptiTrack) Search (Web)Application (+OGRE 3D Engine for the 3D

Representation)

Extension of Antonius Fetch ReferenceImages from the Search-Application Write Tracks into Database of the Search-Application Save TrackImages on the HDD, Write Information into DB

Search Web-Application Offers overview over all indexed objects Presents the location of an object

System

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Textual representation of search results: An object can be located in multiple different areas

(nested/overlapping areas) The areas have to be generated somehow Our textual representation orders and concatenates the areas

according to their sizes (descending)

Example: Gebäude 2 > Raum 12 > Tisch

System: Textual Representation

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2D representation is realized by a map in which a red dot marks the location

Map is needed (true to scale) Map has to be adjusted to the coordinates of the search engine No user position

System: 2D Representation

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3D Representation is realized by using the OGRE Engine Used SketchUp8 for creating model of the room Room had to be mapped to the coordinates of the search

engine Object is represented by a white box

System: 3D Representation

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Images created by the indexing engine are shown to the user The indexing engine stores the image when it recognizes a

known object Therefore the image shows the object in its last known position Our implementation throttles the storage of these images (per

object) => to prevent flooding of many similiar images

System: Last Seen Image

Example Image

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About 20m² room Multiple shelfes, tables Trash objects (to distract user from

searched object) Searched Objects categorized in

small, medium, large

User Study: Apparatus

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Participant reads/signs consent form

User study is recorded if participant agreed

Participant gets introduced to web application and the room itself Important furniture is introduced explicitly to ensure that the

participant is able to use the textual representation Participant searches multiple items (see next slide) Participant fills in TLX / SUS forms

User Study: Procedure I

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Procedure for each search:

Placement of the searched object absent from the participant

Participant is told which object he has to search

Measure time which participant needs to understand where the object could be (Time from telling the object until he leaves the computer/stands up)

Participant enters the room and touches the searched object

Time between standing up and touching the object is measured

Searched object gets removed from the test setup

User Study: Procedure II

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The webapplication

Results after clicking on searched object

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User study with 28 participants 19 male (67.9%) - 9 female (32.1%) Average age 26.1(SD = 6.55) ranging between 11 and 44 Students, doctors, academics, pupil

User Study

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Frequency of search for objects

Participants

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We asked the participants to rank the representations:1. 2D with 82 points (10 times first place)2. 3D with 79 points (9 times first place)3. Last Seen Image with 62 points (5 times first place)4. Text with 57 points (4 times first place)

User Study

2d 3d Last Seen Image Text0

102030405060708090

Ranking

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almost same score very high SD and SE

User Study – NASA TLXRepresentation Text

Last Seen Image 2D 3D

average 28,29 27,68 23,54 24,5median 22 23 21 22SD 19,166 16,596 14,222 14,881SE 3,622 3,136 2,688 2,812

Text Last Seen Image 2D 3D0

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40

60

80

100

120

TLX-Scores (average)

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almost same score high SD and SE

User Study – SUSRepresentation Text

Last Seen Image 2D 3D

average 74,82 79,20 80,71 78,39median 76,25 80,00 83,75 80,00SD 17,11 12,25 14,65 14,07SE 4,14 3,50 3,83 3,75

Text Last Seen Image

2D 3D0.00

10.0020.0030.0040.0050.0060.0070.0080.0090.00

100.00

SUS-Scores (average)

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almost same time 3D needed more because

of switching toOGRE 3D Engine

User Study – Time to understandTTU Text

Last Seen Image 2D 3D

General 9,95 10,67 9,42 12,64Female 11,64 10,51 11,19 14,66Male 8,97 10,46 8,39 11,63SD 3,75 4,46 4,13 5,65Median 8,80 9,30 8,50 11,00

Text Last Seen Image

2D 3D0.002.004.006.008.00

10.0012.0014.0016.00

Time to understand (averages – in seconds)

Female Male General

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almost same time Last Seen Image has

„best“ average

User Study – Task completion timeTCT Text

Last Seen Image 2D 3D

General 5,69 5,45 5,95 5,62Female 5,95 4,89 5,74 5,17Male 5,60 5,68 5,95 5,85SD 1,38 1,05 2,46 2,41Median 5,40 5,35 5,10 5,30

Text Last Seen Image 2D 3D0.001.002.003.004.005.006.007.00

Task completion time (averages - in seconds)

Female Male General

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No clear results

Every human beeing has its own preference

Comparison of the implementation overhead

Conclusion

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Expansion of the scenario

Move the indexing engine to a wearable device (e.g. Google Glass)

Enable mobile devices to search for objects

Future Work

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Thank you very much…Sänk ju wäri matsch

Senk yu veri maç

Le fin