Chessmen Position Recognition Using Artificial Neural Networks

Jun HouDec. 8, 2003.

ScenarioHidden pieces

Fig 1. Illustration of hidden pieces

Augmented Reality chess game – detect the position of all black chess pieces Problem – moving camera, hidden pieces

Constraints: 1-2 piece moves each time;Initial position knownProblem reduced to: whether there is a piece on a given square

Task – Generate synthesized images, train ANN, test recognition rate

Feature SelectionNormalized chessboard squares

In 2D: find region of interest – the chessboard, calculate 3D positions.In 3D: divide each chessboard square into m*m smaller squares (64*m*m)Map 3D square positions onto 2DUse average of each square as input

Camera angles and chessboard square positions

To compensate for the black ratio differencePrior probability of each square occupied by a chess piece

Neural Network Design

m*mImage data

m*m input

Square position

2 input

Camera parameters

6 input

Prior probability of the square

1 input

Fig 2. Neural Network Design

ANN applied:

(1)Tradition GD;

(2)Gradient Descent with variable learning rate;

(3)Gradient Descent with momentum

Experimental Results – Data generation and preprocessing

Use Blender Version 2.30 to model the pieces and use Python scripts to generate the synthesized images

Change the chessboard state – play one random move each time, and observe the chessboard from different viewpointsImage properties – clean, high contrast, little noise, no distortion, no lighting variation.Generate 1500 images with 640*480 resolution

Data filteringUse threshold to select the squares that have at least a portion of black, exclude completely blank squares for training

Experimental Results – Data generation and preprocessing (cont.)

Fig.3 (a) Original synthesized 640 * 480 3D image and (b) converted 64*64 2D image. Only none white squares are used for training. Trying to restore the 3D image to 2D image. The 2D chessboard looks as if rotated 45.

(a) (b)

Experimental Results – Train ANN

Fig.4 Training using GD with variable learning rate

Recognition rate: 72%, GD with variable learning rate 1.05

No significant recognition rate difference between the GD, GD-VLR, GD-M

GD-VLR and GD-M are faster than GD

Fig.5 Training using GD with momentum

Experimental Results

Fig.6 Training set accuracy and test set accuracy. GD with variable learning rate

The more training samples, the more similar are the training and test sets

As the number of training samples increases, the training set accuracy and test set accuracy merge

Conclusion and Future WorkConclusion

Variations of Feed Forward ANNs are used to recognize the positions of chess pieces.

Future workAdjusting ANN parameters

Size of square divisions, effect of randomizing the training data, learning rate, momentum, etc.

Use Confidence measureUse constraints to help recognition

Look at successive frames to find out the piece moved; use frames of different viewpoints for one chessboard state, etc.

Thank you! *^_^*