Hand Gesture Recognition Update - 1


It's been more than two months since I completed the first version of m hand gesture recognition system but I didn't update it on the blog because I got busy with placement season at the college and then the mid-semester exams.

In my last post I tried background subtraction but I could not successfully resolve the problems caused by it i.e. : Background noise,inconsistent convex hull and missing frames.
So, I decided to go with something simpler first like static image filtering which I found here.
I followed the following steps:

  1. Pre process the video frame by converting it to HSV color space.
  2. Change the Hue , Saturation and Value variables to segment the hand.
  3. Post process the frame to remove background noise by doing a bitwise AND of consecutive frames and using erode and dilate functions.
  4. Find the contours and convex hull of the hand segment.
In order to identify the gestures to play Pacman, I divided the screen into 9 parts and assigned 1 part to each gesture i.e. LEFT,RIGHT,UP,DOWN and left the rest of them as blank spaces as shown in the figure below.


Then I used the top most point of the convex hull having the largest area(let's call this point T) as a pointer to identify the gesture. If T is in the LEFT region the pacman moves left , if T is in TOP region the pacman moves up and so on.
I mapped the point T with the respective keys on the keyboard and it worked quite well and the game was playable as is shown in the video below.






I realize that this method is a little simplistic but it is good as a starting point for further improvements.
In order to make further improvements in this model or propose a new model,  I first need to read some more about existing methods.
These would be a good starting point:

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