Backgammon checkers and dice detection first of all thanks for reading, and sorry for such a generic question.
If it is off topic for your forum i would be thankful if someone could point me to a place where i can find an answer.

I am evaluating developing a software to read backgammon positions out of a video or sequence of still images

Backgammon players use to record their matches with a video camera and later manually enter all moves and dice rolls to a computer software for analysis of their errors.

I am thinking of a software that analyzes the video or image sequence and recognizes dices and positions of checkers before and after the move producing an ascii output with dice rolled ad moves made.

Cameras are mounted in a fixed position, pictures could eventually be triggered by hitting the clock when playing with a chess clock

Is this feasible ( i guess yes )? Has anyone an idea of the effort i can expect to get this done.
If it fits my budget i would like to start a freelance project...

Thanks again for your help!

Best regards

wolf
Is this feasible ( i guess yes )?


Yes


  Has anyone an idea of the effort i can expect to get this done. If it fits my budget i would like to start a freelance project...


The main tool you are going to need is the Hough Transform. Out of the box, the Hough Transform converts straight lines to points (I will talk about circles in a minute). Therefore, the task of detecting the frame of the board and the lanes becomes trivial.

Detecting the pawns, is also trivial with the circle Hough transform. In this case, we are looking for stable configurations of strong point features at some radius.

Detecting the dice is also trivial, especially for "proper" dice (with dots, not numbers), with a simple normalised cross correlation application.

The main challenge of the whole thing is getting rid of perspective. It doesn't create a huge amount of problems as it simply distorts (in a predictable way) the points where the Hough Transform is maximal. But to keep things simple it would be good if you were to use a top-bottom looking camera. So, the camera is looking the board vertically, from above.

Another thing that will help immensely in discriminating your desireable targets is thresholding followed by edge detection. In fact, this step will improve the output of the Hough Transform too.

Hope this helps.