To translate the gesture performed by the user in a
video sequence into meaningful symbols/commands, feature
extraction is the first and most crucial step in such systems
which measures the detected hand positions and its movement
track. We propose an efficient approach based on inter-frame
difference (IDF) to handle the hand movement tracking, which
is shown to be more robust in the accuracy aspect compared to
skin-color based approaches. Computational efficiency is
another attractive property that our approach greatly
improves the processing frame rate to fulfil the demand of a
real-time hand gesture recognition system.
11. Reference Paper
Movement Tracking in Real-time
Hand Gesture Recognition
Authored by
Hong-Min Zhu & Chi-Man Pun
Department of Computer and Information Science
University of Macau, Macau SAR, China
{ma86560, cmpun} [at] umac.mo
9th IEEE/ACIS International Conference on Computer
and Information Science
12. Reference Paper
What does it say?
This paper deals with overcoming
of SCHD technique for Hand
Gesture Recognition using newly
improved Algorithm, IFDHD
14. Previous Work Done
Temporal Hand Gesture
Assumptions
Camera User Synchronization
Uniform Lightening Condition
Simple Background Features
Frame Rate – Gesture Speed
Coordination
15. S C H D
Skin Color based Hand Detection
J. Kovac and P. Peer – Designed Skin
Classifier
Rules
Pixel is classified as a skin pixel if:
Value of Red > 95, Green > 40 and Blue > 20
&
max{R, G, B} - min{R, G, B} > 15 &
|R - G| > 15 and R > G and R > B
16. Proposed Solution
Problems with SCHD
Computationally Expensive
Skin-like Object Ambiguity
Illumination Parameters
Skin Color Variation
Solution – Motivated from BSHD
IFDHD
Inter-Frame Difference based Hand Detection
19. Algorithm for Hand Detection
Input: Frames Fi = 1..N from video segment
Steps:
1. Convert frame F1 to grayscale
2. Repeat (until end of video segment)
1. Convert frame Fi to grayscale
2. Intensity difference image D0 = |Fi – F1|
3. Binary image I = (D0 > T0)
4. Do image opening on I followed by closing
5. Splitting of Large regions into max size boundary
box
as 60x80
6. Calculate center co-ordinate
39. Algorithm for Movement
Tracking
Input: Region centers Detected in each frame
Steps:
1. Initialize the start of frame
2. Repeat (for each frame > 1)
1. Identify tail locations and store
2. Calculate matrix of distances between centers and tail
locations
3. Repeatedly select – min(Distance ( I ), Distance ( J ))
4. If Distance( I ) < Threshold then append Center to Gesture
and delete Distance( I ) Else initialize new Gesture start
location
5. Select Gesture Frame that has the maximal standard
deviation
51. Reference Paper
American Sign Language Recognition
System for Hearing Impaired People Using
Cartesian Genetic Programming
Authored By
Fahad Ullah
Department of Computer Systems Engineering,
University Of Engineering & Technology,
Peshawar, Pakistan
Proceeding of 5th International Conference on
Automation, Robotics and Applications, New
Zealand
52. Application Scenario
Why the interfaces are changing ?
How many Apps Out there?
Have you tried X-box,PSP-2,Mac-OSX
January 9, 2012, 66 million Xbox 360 consoles
have been sold worldwide.
New era of Interfaces
53. Application Scenario
What if you can’t speak?
ASL
CGP (Cartesian Genetic Programming)
How it works?
Genetic programming an Overview:
Probabilistic search
Darwinian principle of natural selection
Naturally occurring genetic operations such
as crossover and mutation.
54. • Better individuals are preferred
• Best is not always picked
• Worst is not necessarily excluded
• Nothing is guaranteed
• Mixture of greedy exploitation and
adventurous exploration
• Similarities to simulated annealing (SA)
Probabilistic Selection Based
On Fitness
57. ASL using CGP
26 English language alphabets are trained
and Identified
The system uses 26 binary images
representing the different alphabets
Mentioned system with a Dictionary
correction ability in order to increase the
overall accuracy of the system.