Design of an efficient multi-objective recognition approach for 8-ball billiards vision system

Jiaying Gao, Qiuyang He, Hong Gao, Zhixin Zhan, Zhe Wu

Abstract


In this paper, some key technologies based on colour image processing for 8-ball billiards robot vision system are discussed and an efficient approach for multi-objective recognition is proposed. This approach is divided into two parts, i.e. multi-objective detection and ball pattern recognition. In image pre-processing, the normalized RGB colour space and histogram statistics are adopted for segmentation of background (table cover) and foregrounds. In order to accurately locate and isolate the single ball in a local region, the improved Hough Transform (HT) algorithm and the Least Squares (LS) method are adopted in combination. The improved HT algorithm is used for the purpose of eliminating the noise concentrated at edge points, and the LS method is used for fitting the circle center accurately with the least mean square error. Based on single ball detection in a local region, the multi-ball detection approach has been worked out to locate the position of each ball on the table. In the experiment, the proposed approach has been proved to complete the detection with an accuracy of 99.4% in 0.65s in average, and the performance is better
than the traditional Circular Hough Transform (CHT) algorithm and the K-means cluster method. In addition, the Convolution Neural Network (CNN) method is adopted for pattern recognition of each target ball being segmented, i.e. identification of a solid ball or a striped ball. In order to improve the quality of CNN training: the colour segmentation and morphologic operation are applied for the segmented ball image pre-processing; the training set images are rotated for augmentation; pre-training stage is introduced in for optimizing the initial weight matrices. The calibrated image blocks are imported to the network for training. In the verification test, the trained CNN model shows a recognition rate of over 98.5%, and outperforms the other three classic methods. The introduction of CNN method has been proved to be correct and effective, and is an innovative and significant step
for the design process of the 8-ball billiards robot vision system. 


Keywords


Billiards recognition; Normalized RGB colour space; Improved Hough Transform algorithm; Least Square (LS) Method; Convolution Neural Network (CNN).

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References


Ho, K.H.L., Martin, T. & Baldwin, J. (2007). Snooker

Robot Player - 20 Years on. IEEE Symposium on

Computational Intelligence and Games (CIG), pp. 1 -8.

Ling, Y., Li, S., Xu, P. & Zhou, B. (2012). The detection

of multi-objective billiards in snooker game video.

IEEE International Conference on Intelligent Control &

Information Processing (ICICIP), pp. 594 -596.

Shen, W. & Wu, L. (2010). A method of billiard objects

detection based on Snooker game video. IEEE International

Conference on Future Computer and Communication

(ICFCC), V2- 251 - V2 -255.

Wu, L., Liu, J., Cheng, Z., Wang, H. & Liu, Q. (2010). An

effective multi-object detection approach. IEEE International

Symposium on Intelligent Signal Processing and

Communication Systems (ISPACS), pp. 1 -4.

Legg, P., Parry, M.L., Chung, D.H.S. & Jiang, R. M.

(2011). Intelligent filtering by semantic importance for

single-view 3D reconstruction from Snooker video. IEEE

International Conference on Image Processing (ICIP), pp.

- 2388.

Shih, C. & Chu, W.C. (2007). A Vision Based Interactive

Billiard Ball Entertainment System. Proceedings of the

First IEEE International Workshop on Digital Game and

Intelligent Toy Enhanced Learning, pp. 200 -202.

Takahashi, M., Kasai, T. & Suzuki, Y. (2008). Support

system for pocket billiards. SICE Annual Conference,

IEEE, pp. 3233 – 3236.

Höferlin, M., Grundy, E., Borgo, R., Weiskopf, D.

& Chen, M. (2010). Video Visualization for Snooker

Skill Training. Computer Graphics Forum 29(3): pp.

–1062.

Gao, J., He, Q., Zhu, M., Liang, H. & Guo, X. (2015).

Design of the multiple Neural Network compensator

for a billiard robot. IEEE International Conference on

Networking, Sensing, and Control (ICNSC), pp. 17 -22.

Gao, J., He, Q. & Zhan, Z. (2017). Design of neural

network controller for a billiard robot. Journal of

Beijing University of Aeronautics & Astronautics,

(3): 533 -543.

Cheng, B.R., Li, J.T. & Yang, J.S. (2004). Design of

the neural-fuzzy compensator for a billiard robot. IEEE

International Conference on Networking, Sensing, and

Control (ICNSC), Vol.2, pp. 909 -913.

Subban, R. & Mishra, R. (2012) Rule-based face

detection in colour images using normalized RGB

colour space— A comparative study. IEEE International

Conference on Computational Intelligence & Computing

Research (ICCIC), pp.1- 5.

Dargham, J.A. & Chekima, A. (2006). Lips Detection in

the Normalised RGB Colour Scheme. IEEE International

Conference on Information & Communication

Technologies, pp.1546- 1551.

Cuevas, E., Wario, F., Osuna-Enciso, V. & Zaldivar ,

D. (2012). Fast algorithm for multiple-circle detection on

images using learning automata. IET Image Processing

(8): 1124- 1135.

Chen, X., Lu, L. & Gao, Y. (2012). A new concentric

circle detection method based on Hough transform.

IEEE International Conference on Computer Science &

Education (ICCSE), pp. 753758-.

Lau, M.M., Lim, K.H. & Gopalai, A.A. (2015).

Malaysia traffic sign recognition with convolutional neural

network. IEEE International Conference on Digital Signal

Processing (DSP), pp. 1006 -1010.

Verma, A. & Vig, L. (2014). Using Convolutional Neural

Networks to Discover Cogntively Validated Features for

Gender Classification. IEEE International Conference

on Soft Computing & Machine Intelligence (ISCMI),

pp.33- 37.

Kumar, V. D. A., Ramakrishnan, M., Malathi, S. &

Kumar, V. D. A. (2015). Performance improvement

using an automation system for recognition of multiple

parametric features based on human footprint. Kuwait

Journal of Science, 42.1(2015):109 -132.

Nallamuth, R. & Palanichamy, J. (2015). Optimized

construction of various classification models for the

diagnosis of thyroid problems in human beings. Kuwait

Journal of Science, 42 (2):189 -205.

Gao, J., He, Q., Zhan, Z. & Gao, H. (2016). Dynamic

modeling based on fuzzy Neural Network for a billiard

robot. IEEE International Conference on Networking,

Sensing, and Control (ICNSC), pp. 1- 4.


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