ABSTRACT The Virtual Trial Room (VTR) application software simulates an apparel dressing room by the implementation of a virtual mirror, portraying an augmented view of the user with virtual superimposed clothes. Traditional approach to the design and implementation of virtual dressing rooms have been wildly using either normal webcams with Tag/Marker based tracking or expensive 3D depth & motion sensing cameras like Microsoft Kinect. The main idea of this paper is to methodologically devise a novel VTR solution deploying ubiquitous 2D webcams with tag-less tracking, in a real-time live video mode using open source tools and technologies. The solution model implements a tag-less or marker-less Augmented Reality (AR) technique with face detection technology and provides an intuitive motion-augmented User Interface (UI) to the VTR application, in the form of an interactive human-friendly Virtual Mirror using simple hand gestures. A qualitative performance analysis of the application is evaluated at the end of the paper to determine the fundamental susceptibility of the VTR system against varied illumination conditions. https://github.com/Project-VTR

1. Code: CS 893
JIS College of Engineering, WBUT
The Augmented Reality Technique
Abhinav Biswas & Soumalya Dutta

2.  Portrays an augmented view of the user
in a dynamic real-time Live Video mode
with virtual superimposed clothes
 Implements a virtual mirror
with interactive digital options onscreen
 Immediately allows the user to see
if a clothing’s color/style suits his/her body
before he can actually decide to buy it.

3.  User stands in front of the digital screen
Various onscreen options get displayed
Like Category (Men, Women, Kids).
 Selects option using simple Hand Motion
Then the augmented image with virtual clothes is shown.
 Adjusts the placement of the cloth item
With onscreen options like Zoom In/Out, Up/Down etc.
 Finally take a snap, save or print it.
Also now the user can decide whether the cloth is worth a buy.

4.  Face Tracking, Motion Detection
OpenCV 2.3.1
 Swing Java 1.6,
Front-end GUI.
 JavaCV – The OpenCV Wrapper
With hardware accelerated image processing features.
 Eclipse Helios 3.6
The wonder tool, An extensive IDE
OpenCV

5. Grab video frame from Webcam
& flip it.
Track selected options based
on detected motion & act
accordingly
Options
Adjustment of the
overlay
Overlay replacement,
(Next/Previous)
Motion Detection using
frame difference method
Scaling &
Positioning
Show frames in a video mode
Implement Face Detection on
each frame using
HaarCascade classifier
Super Impose virtual cloth
image on the frame based on
detected face
Parallel Processing

6.  Taking a Large No Samples
 Creating the Description file
 Packing the positive samples
into a vector file
 Training the classifier
 Converting the trained
cascade into a cascade file

7.  Absolute difference of
consecutive frames with
threshold value.
 Detection of Hand motion in
pre-defined areas.
 System response based on
selected option.

8. 
 Retail Industry – Shopping Malls
 Online Web Shopping

 one-click share on Facebook.com
 virtual mirror for eye-wear (sunglasses) & necklace
 Automated smile detection for easy snapshot.
 Auto-suggestion of cloth items, etc.

9. Mr. Abhinav Biswas,
Mr. Soumalya Dutta
http://abhinavtheprince.blogspot.com