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IS480 Team wiki: 2012T1 M.O.O.T/Midterm Wiki

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Project Progress Summary

Team M.O.O.T has progressed steadily since Iteration 1 -which was kick-started post Acceptance presentation; and is currently in Iteration 4, which will last up to the end of the midterm week. We also encountered a major obstacle in coming up with an alpha version of Artificial Neural Network to determine gender in Iteration 1. There was also a change in client's requirement, contributing to the delay. Confirmation of final requirement was achieved in Iteration 2, enabling team to focus and hence team managed to catch up in Iteration 2.

By midterm, team is confident of completing 70% of AlterSense features. These features include gender recognition and basic photo taking functionalities. After midterm, team will work on implementing the narrative interaction features and refining of AlterSense in general. Team is therefore confident of delivering the complete AlterSense solution by the end of week 13.

Project Highlights

  • Inclusion of machine learning
  • Internal addition of stakeholders: requirement to liase with Marcom team
  • Requirement changes:
    • AlterSense to enable photo taking
    • Gender-related content will not be limited to promotions only
    • Interaction with Techy & Marlon to be replaced with photo-taking related narrative, which translates to starting over from scratch
    • Analytics possibility provided by Microsoft Tag will be independently explored by CapitaMalls Asia
  • Took 3 weeks to set up alpha version of Neural Network instead of only 2 weeks
  • Omitted Waist-Hip-Ratio and detection of bag for gender recognition; for Kinect does not detect hip, and depth measurement to detect bag interferes with the detection of arm joint

Project Management

Project Status

S/N Feature Status Confidence Level (0-1) Comments
Photo taking
1 Detect stationary shopper Deployed & tested 1
2 Display shopper’s silhouette Iteration 5 0.9 Implemented for Acceptance version
3 Display doors with enticing scene Iteration 5 0.8 Inserted image too for Acceptance, but its activation may require more work
4 Detect shopper's gesture (reaching for a particular door) Iteration 5 0.7
5 Display change in chosen door Iteration 5 0.7
6 Display augmented reality scenery background Deployed & tested 1 Refinement completed as well
7 Inserting instruction thought bubble Deployed 0.99
8 Detect shopper raising right hand Deployed 0.99
9 Countdown timer Deployed & tested 1
10 Take a photo Deployed & tested 1
11 Display photo Deployed & tested 1
Gender Recognition
12 Measure height Deployed & tested 1
13 Measure shoulder & hip width Deployed & tested 1
14 Detect presence of long hair Ongoing 0.5
15 Detect presence of skirt Deployed 0.6
16 Determine gender of shopper Deployed & tested 1
Promotions, Photo Gallery & Microsoft Tag
17 Determine type of promotions to display Ongoing 0.75
18 Create tag Deployed 1
19 Overlay of tag Deployed 0.85 Position to be adjusted
20 Exit screen Deployed & tested 1
21 Photo Gallery Deployed 0.9
22 Read tag Deployed 0.95 Implemented for Acceptance
23 Update tag Iteration 6 0.9 Implemented for Acceptance
24 Delete tag Iteration 6 0.9
25 Integrate tag with AlterSense Iteration 6 0.9
26 Retrieve analytics raw values from Microsoft Tag web service Iteration 6 0.95 Implemented for Acceptance
27 Parse analytics values Iteration 6 0.95 Implemented for Acceptance
28 Display parsed analytics values Iteration 6 0.95 Implemented for Acceptance

Project Schedule

Iterations Planned Actual Comments
1 Explore Classifier Algorithm Week 1 Week 1 Found out about Neural Network
Primary research: measurement collection 24/08/12 24/08/12 58 participants (30 males, 28 females)
Analysis of measurement collection Week 2 Week 2
Gender differences trend establishment Week 2 Week 2
  • Decided to use height, shoulder width, and probably hip width
  • Secondary research yielded possibility to explore Centre of Moment
Gender recognition based on Waist-to-Hip Ratio Week 2 Dropped Decided to drop Waist-to-Hip Ratio as Kinect cannot detect waist
Neural Network classification to determine gender based on Waist-to-Hip Ratio Week 2 Height measurement & face tracking on Kinect Week 2
2 Neural Network classification & scoring system Week 3 Gender classification based on height & shoulder width Week 3
Physical parameter expansion: neck & shoulder width Week 3 Shoulder width & height passed in as parameters,but not neck Week 3 Behavioural parameters: hasBag, hasSkirt, hasLongHair (related to neck)coded but not passed in as parameters
3 Gender recognition based on physical & behavioural parameters Week 4 Skirt & bag behavioural displayed, but not used for gender recognition Ongoing
Countdown timer Week 4 Week 4
Capturing shopper's outline Week 4 Week 4 Edges refined as well
Capturing of photo Week 5 Week 5
Backward propagation Week 5 Week 5
Saving learning state Week 5 Pushed to Iteration 6
Integration of photo taking with physical gender recognition Week 5 Week 5

Project Metrics

Schedule & Bug Metrics

Team M.O.O.T's schedule and bug metrics can be accessed here.

Gender Recognition Metrics

Our gender recognition metrics can be accessed here.

Project Risks

All risks mentioned during Acceptance Presentation have been mitigated, especially that of technical risks.
Team has also foreseen several new risks:

  • storage and transferring of photos
  • development delay due to members' unavailability
  • inability to respond appropriately to AlterSense
  • granular overlaying of augmented background

For more information on these risks, access team's risk management page.

Technical Complexity

Quality of Product

Intermediate Deliverables

Stage Specification Modules
Project Management Metrics Schedule & Bug Metrics, Bug Log
Minutes Client Meeting,Supervisor Meeting
Content Interaction flow chart Flow Chart
Narrative flow Interactive Content Flow
Requirement Functionality list Functionalities
Design Use Case Use Case
Architectural diagram Architectural Diagram
Testing User Testing 1 documentation UT1

Deployment

Testing

Describe the testing done on your system. For example, the number of user testing, tester profile, test cases, survey results, issue tracker, bug reports, etc

Reflection

Individual Reflection

Back to M.O.O.T's page

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