HeaderSIS.jpg

Difference between revisions of "IS480 Team wiki: 2012T1 M.O.O.T/Project Management"

From IS480
Jump to navigation Jump to search
Line 410: Line 410:
 
||  
 
||  
 
|| Week 7
 
|| Week 7
||
 
|-
 
|rowspan="7" style="text-align: center;"| 5
 
||
 
|| Week
 
||
 
|| Week
 
 
||  
 
||  
 
|-
 
|-
  
||
 
|| Week
 
||
 
|| Week
 
||
 
|-
 
 
||
 
|| Week
 
||
 
|| Week
 
||
 
|-
 
 
||
 
|| Week
 
||
 
|| Week
 
||
 
|-
 
 
||
 
|| Week
 
||
 
|| Week
 
||
 
|-
 
 
||
 
|| Week
 
||
 
|| Week
 
||
 
|-
 
 
|}
 
|}
 
  
 
=== Finals Wiki ===
 
=== Finals Wiki ===

Revision as of 14:18, 16 November 2012

Home

Team/Project Partners

Project Overview

Project Management

Design Specifications

Technical Applications


Project Schedule Schedule & Bug Metrics Gender Recognition Metrics Risk Management Minutes Repository

Post-Acceptance Project Schedule

Planned Schedule Summary

This is the latest schedule amended prior to the beginning of iteration 3. After numerous discussions, we have been informed that CMA would really like to have photo-taking feature. Our primary research of measurement collection in iteration 1 has also shown us that it is not possible to determine gender based on Waist-Hip ratio as waist is not detected by Kinect. The highlight of the schedule amendment is hence the changes of interactive features to photo taking feature and gender recognition based on 4 parameters: height, shoulder length, whether shopper is holding on to a handbag, and whether shopper is wearing long skirt. The focus will be on gender recognition based on neural network until midterm, followed by development of photo taking feature post midterm.


WithPhotoTakingScheduleSummary.png


Access previous planned schedules to view earlier planned schedules prior to firming up of client requirements and primary research on differences between male and female.

Weekly Progress

Week Date Scheduled Features Completed Features Pending Features/Remarks
1 17/08/12 – 24/08/12
  • Explore Classifier Algorithm (CA)
  • Measurement collection
  • Theoretical understanding of Neural Network (NN) and its classification feature
  • Measurement collection
N.A.
2 25/08/12 – 31/08/12
  • Analysis of measurement collection
  • Gender differences trend establishment
  • Gender recognition based on Waist-to-Hip Ratio (WHR)
  • NN classification
  • Analysis of physical measurement collection classification feature
  • Gender differences trend establishment based on physical measurement analysis & secondary research
  • Measurement of height based on image captured by Kinect
  • Found that waist cannot be detected by Kinect, hence dropping WHR
  • Difficulties encountered in implementing NN classification
3 01/09/12 – 08/09/12
  • NN classification & scoring system
  • Parameters expansion: neck & shoulder width
  • Passing of height and shoulder width captured by Kinect to NN
  • NN classification based on height and shoulder width
  • Decided to include parameters hasBag & hasSkirt instead of neck width in the upcoming week
4 09/09/12 – 16/09/12
  • Gender recognition based on complete set of inputs
  • Capturing shopper’s outline
  • Countdown timer
  • Gender recognition based on height & shoulder width
  • hasBag & hasSkirt displayed for consideration
  • Countdown timer
  • Refining of shopper's outline
  • hasSkirt, hasBag parameters to be improved and integrated with passing in of parameters through Kinect
5 17/09/12 – 23/09/12
  • Capturing of photo
  • Backward propagation
  • Saving learning state
  • Integration of photo taking with gender recognition
  • Capturing of photo
  • Backward propagation
  • Integration of photo taking with gender recognition
  • Saving learning state is pushed back to Iteration 6
6 24/09/12 – 29/09/12
  • Microsoft Tag
  • Advertisement Management System page
  • Refining overlaying of augmented background
  • Integration of Microsoft Tag into photos
  • Addition of behavioural parameters for gender recognition
  • Integration of gender recognition (with behavioural parameters) with photo taking
  • Microsoft Tag
  • Advertisement Management System page
  • Refining overlaying of augmented background
  • Integration of Microsoft Tag into photos
  • Addition of behavioural parameters for gender recognition
  • Integration of gender recognition (with behavioural parameters) with photo taking

Behavioural parameters were not very accurate

7 01/10/12 – 06/10/12
  • Using real figures instead of Kinect figures
  • Addition of behavioural parameters for gender recognition
  • Integration of gender recognition (with behavioural parameters) with photo taking
  • Integration of Microsoft Tag, gender recognition, and photo taking
  • Using real figures instead of Kinect figures
  • Addition of behavioural parameters for gender recognition
  • Integration of gender recognition (with behavioural parameters) with photo taking
  • Integration of Microsoft Tag, gender recognition, and photo taking
N.A.
8 08/10/12 – 12/10/12
  • Scene transition
  • Display shopper's silhouette
  • Make callout bubble dynamic
  • Code clean up
  • Scene transition
  • Display shopper's silhouette
  • Integration of outline-photo-taking-displaying photo with tag
  • Make callout bubble dynamic
9 15/10/12 – 19/10/12
  • Choosing of door
  • Unsupervised learning
  • Make callout bubble dynamic
  • Stick man approaching chosen door
  • Debugging of bug: crashing in the wild
  • Choosing of door
  • Auto-rescale callout bubble
  • Stick man approaching chosen door
  • Recording of learning response to database
  • Debugging of head-depth bug causing crash in wild environment
  • Callout bubble position needs to be relative to user's
  • Integration of unsupervised learning to AlterSense
10 22/10/12 – 26/10/12
  • Debug to enable reverting to scene 0
  • Relating door scene image with photo taking scene
  • Integration of unsupervised learning to AlterSense
  • Callout bubble to follow (relative to)user's position
  • Activation of training upon detection of dropping accuracy
  • Relating door scene image with photo taking scene
  • Callout bubble to follow (relative to)user's position
  • Integration of unsupervised learning to AlterSense
  • Debug to enable reverting to scene 0
  • Unsupervised learning to be modified to be less obvious
  • Activation of training upon detection of dropping accuracy
11 29/10/12 – 02/11/12
  • Modified unsupervised learning (Scene 3)
  • Gender-targeted content insertion into AMS
  • Modified display of tag for enhanced user experience
  • Instructional callouts to choose door and scan tag
  • Error control: pick closest skeleton and display out-of-frame instruction
  • Thorough integration for User Testing 2
  • Modified unsupervised learning (Scene 3)
  • Error control: pick closest skeleton and display out-of-frame instruction
  • Gender-targeted content insertion into AMS
  • Modified display of tag for enhanced user experience
  • Thorough integration for User Testing 2
  • Instructional callouts to choose door and scan tag

N.A.

12 05/11/12 - 09/11/12
  • Improve display of hat
  • Improve grabbing of hats
  • Self healing if accuracy drops
  • Solve raising of hand message bug
  • Solve door and tag scanning instructions bug
  • Solve raising of hand message bug
  • Increase size of hats so that they will be more noticeable by shoppers
  • Enable grabbing of hat even when shopper's elbow is not higher than shopper's shoulder
  • Solve door and tag scanning instructions bug
  • "Self healing if accuracy drops" is scrapped as reliability of hat-picking scene for gender validation was low, at 27% based on UT2
13 12/11/12 - 15/11/12
  • Instruction to stand still in Scene 0: galaxy scene
  • Hat-picking: right hand for female hat, left hand for male hat
  • Retrieving of tag information from Microsoft Server
  • Parsing of tag information
  • Display of tag information in AMS

Project Planned vs Actual 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
4 Microsoft Tag creation Week 6 Week 6
Advertisement Management System page Week 6 Week 6
Refining image overlaying Week 6 Week 6
Incorporate tag into photo Week 6 Week 6
Gender recognition: physical + behavioural Week 6 Week 7
Integration of gender recognition (physical+behavioural) with photo taking Week 6 Week 7

Finals Wiki

M.O.O.T Final Wiki
1. Project Progress Summary
1.1. Project Highlights
1.2. Project Challenges
1.3. Project Achievements
2. Project Management
2.1. Project Schedule
2.2. Project Metrics
2.3. Technical Complexity
3. Quality of Product
3.1. Project Deliverable
3.2. Quality
3.3. Deployment
3.4. Testing
4. Reflection
4.1. Sponsor Comment
4.2. Team Reflection
4.3. Individual Reflection

Midterm Wiki

M.O.O.T Midterm Wiki
1. Project Progress Summary
1.1. Project Highlights
2. Project Management
2.1. Project Status
2.2. Project Schedule
2.3. Project Metrics
2.4. Project Risks
2.5. Technical Complexity
3. Quality of Product
3.1. Intermediate Deliverables
3.2. Deployment
3.3. Testing
4. Reflection
4.1. Team Reflection
4.2. Individual Reflection

Pre-Acceptance

MOOTschedule.jpg