Final post

We've just had our final presentation yesterday and everything went very nice!

As seen above, that is the final product we built, and we have decided to call it Ambinote.

We are starting to write a paper now for our final submission now..

Check out the video of how our interactive device works.

"Access notifications in 3D space using familiar gestures" 

Skin Detection is actually great.. for this problem at least

The building block for our system was represented by a color detector extended to support skin detection, in the HSV space. The reason why we prefered HSV to RGB was the fact that the first space separates the image intensity from the color information, which proved critical when implementing a color detector. Even though there are many spaces which have the notion of separation mentioned above, we used HSV due to existing implementations to convert RGB to HSV and viceversa.

Advantages:

Due to the fact that we had fog in the frame, using a different technique to detect the hand, for example motion detection or background subtraction, would had resulted in a large number of false positives. This effect would have rendered the system highly inaccurate, triggering the notification system even when the user’s input was not actually registered.

Drawbacks:

The main drawback of this system is the fact that we are considering skin detection to be a color detection problem. We have set the HSV values for ranges of pixel intensities that we considered to be skin, which could cause inaccuracies. For example, only a set of skin tones can be detected, while others will not be detected. This variable assumes that we have a priori knowledge of the range of skin tones we want to model. Furthermore, if the working background consists of objects with color values in the range we specify, such as wood, then our system will yield false positive results, as seen in the picture posted above.

Problems with the MotionDetector

Until now, I have been testing the camera and motion detector using just my laptop's built-in camera, which worked fine. Problems occurred when we tied up all the systems together.

As you might recall, I am detecting motion by using a background subtraction method, which checks whether there is change in pixel intensities between two frames. If that occurs, than that portion of the frame is regarded as not belonging to the background, thus it is moving. The problem with this approach, as I had found out, was smoke. Since our setup involved having the USB Camera tied up on the box, it would be very close to the smoke emitted. Because smoke was dense, it would be picked up as movement, thus rendering false positive results.

After having tried many ways to threshold the results produced by the smoke, we decided it would not work in a way which would be reliable, so we decided to re-implement the system using another method..