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Pool Table Edge, Pocket and Ball Position Estimation, for Cue Guiding

Shaun Weatherford (sweather@stanford.edu)

Pool and billiards games are of interest in object detection from several perspectives: finding the

edges of the table can be challenging, as the edges may be covered in the same colored felt as thetable top; likewise, pocket detection can be difficult if the pocket is also of the same color felt asthe table top; isolating each ball from others may be difficult if balls are touching and low look

aspect causes ball occlusion; determining solid balls versus striped balls may be problematic

under unusual ball orientations and lighting conditions (giving rise to glare reflections).

Using the detected table edges, pocket positions and ball position and colors to calculate optimal

cue guidance necessitates more precision than simple detection; characteristics like table edge

orientations and ball edge normal vectors must be very accurately known if edge bank shotsand/or oblique ball impact angles are considered.

In this project, techniques for table top, edge, pocket and ball detection will be explored, undervarious adverse conditions of table lighting, look aspect angle, and consequent ball characteristic

occlusion. Of primary consideration will be table top look aspect angles from directly above the

table. Time permitting, methods to account for lower aspect angles will be considered, withtechniques to estimate the look angle from salient table features, and thus better estimate ball

positions.

A cell phone will be used to capture pictures of the pool table and ball positions. These pictures

will be sent to a laptop computer, on which Matlab algorithms will be used to process the

images, and superimpose on the image a cue guide vector. Pictures will be taking from directly

above the table, to aspects closer to those approximating what a typical pool player would see.

Also, pictures will be taken under different table lighting schemes, from diffuse to more direct(causing glare reflections on balls). Finally, pictures will be taken with different ball positions

and orientations.

Estimating cue guide angles in order to optimize techniques such as ball spin, and multiplecolor/stripe ball impact will not be covered in this project.

References

[1]Stochasticks: augmenting the billiards experience with probabilistic vision andwearable computers

Jebara, T.;Eyster, C.;Weaver, J.;Starner, T.;Pentland, A.

Wearable Computers, 1997. Digest of Papers., First International Symposium on

Digital Object Identifier:10.1109/ISWC.1997.629930

Publication Year: 1997 , Page(s): 138 145

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[2]The detection of multi-objective billiards in snooker game video

Yan Ling;Shaobin Li;Pin Xu;Bin ZhouIntelligent Control and Information Processing (ICICIP), 2012 Third International Conference on

Digital Object Identifier:10.1109/ICICIP.2012.6391406

Publication Year: 2012 , Page(s): 594

596

[3]Support system for pocket billiards

Takahashi, M.;Kasai, T.;Suzuki, Y.

SICE Annual Conference, 2008Digital Object Identifier:10.1109/SICE.2008.4655222

Publication Year: 2008 , Page(s): 3233 - 3236

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