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Journal of Information, Control and Management Systems, Vol.11, (2013), No. 1 35

LASER SPOT DETECTION

Matej MEKO, tefan TOTH

University of ilina, Faculty of Management Science andInformatics,Slovak Republic

e-mail: [email protected],[email protected]

Abstract

The paper proposes method for detection of laser spot emitted by a laser pointer.

A camera captures projection screen on which is displayed image from a digital

projector and also laser spot from a laser pointer. Our developed algorithm can

effectively detect red and green laser spot in real-time using image processing

and by setting up camera to be captured high intensity of laser beam. To

demonstrate the workings of our method we developed a simple game similar to

Duck Hunt game in which main goal is to shoot a duck by laser pointers.

Keywords: laser pointer, laser pen, detection, duck hunt game, image processing

1 INTRODUCTION

Laser pointer (or laser pen) has become a common tool used during a

presentation. It is a small device that emits an intense beam of light over a long or

narrow distance. One can use it to highlight something for example to point out

important parts on a slide of presentation. In [1] authors used it for guidance using

librarian robot. The robot points out position or direction to a target. Other usagepresents [2] shooting simulator using photodiode sensors to detect presence of laser.

Detection and localization of the laser spot on a projection screen using a cameracan be an interesting task. We could use laser pointer as a mouse [3] or use it to

execute some actions based on gesture recognition (run an application, move to

next/previous slide, etc.). Methods for detection of laser spots can be various. In [4]

authors used template matching and genetic algorithms. Other approach uses

frequency-demodulation CMOS sensor [3]. Authors in [5] even implemented

algorithms on hardware using FPGA.

In this paper we would like to propose our method for laser spot detection using

proper camera settings on the projection screen. The following sections describe issues

and restrictions produced by a camera. Then we present the detection algorithm of laser

spot and a game that demonstrates the workings of the implemented algorithm.
mailto:[email protected]:[email protected]

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36 Laser Spot Detection

2 LASER POINTER DETECTION

Laser pointers may have various colors of the laser beam (the most common is

red or green color). Therefore a detector of laser spot can focus on color values ofpixels. It searches pixels in input image that contains color in specified range. The

main issues of laser spot detection on projection screen bring about a change of light

condition during application execution, projection of animations, projector power, laser

pointer power, and quality of camera (image sensor, image resolution and camera

FPS). However the main problem causes the camera chip. The chip is not as sensitive

as the human eye. For example a laser spot is seen by human eye as red dot, while the

camera see it as white. This is due to the camera chip, which cannot process higher

light intensity because signal is truncated and turned into highest possible value i.e.

white color.

This problem can be partially reduced by adjusting of the camera exposition.Reducing exposition of the camera reduces time during scene is captured. So only the

strongest sources of light will be captured.

2.1 Laser color

Correct exposure configuration of the camera can improve color detection of laser

spot. Figure 1b shows a laser spot captured by a camera at correct exposure settings.

As you can see only the edge of the laser spot contains information about color since

middle of laser spot is too much brightness. So to determine color of laser spot we can

use pixels near to the brightness middle (Figure 1c gray area). Size of this area depends

on the ambient light conditions, exposition configuration and power of laser source.

Figure 1 Appearance of red laser spot a) at incorrect exposure settings

b) at correct exposure settings c) zoomed in at correct exposure settings (white color

represents pixels with high value in HSV value channel, gray area is used for

recognition of laser color)

2.2 EmguCV and OpenCV

We chose the .NET framework and C# programing language to create a test

application since allow easy and rapid development any application. In order to image

processing we used EmguCVlibrary. It wraps OpenCVlibrary written in C/C++, so it

can be used in various .NET programing languages.

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OpenCVis a huge open-source library oriented for image processing. It contains

many high optimized functions for real-time computer vision oriented applications.

This library is multi-platform and it can be used in such operating systems as

Windows, iOS, Linux, Android etc.

2.3 Camera settings

At first we researched how to programmatically adjust the camera settings,because automatic adjusting is defiantly turned on which is not good for image

processing (detection algorithms need to have stable focus or exposure, which can be

ruined using automatic settings). Creating library for wide camera configuration was

first step. We used the library DirectShowLib successfully which is wrapper for key

COM objects IAMCameraControl or ICameraControl. They provide rich camera

control.However, how much settings can be configured depends on the camera quality.

We tested 6 different kinds of web cameras from different vendors. Cheaper cameras

had fewer options to adjust. Finally we decided to use Logitech HD Pro Webcam C920

since it has many options for settings.

2.4 RGB and HSV model

The most commonly used color model in computers is RGBcolor model. In this

model each color is presented by values of three basic colorsred, green and blue. Incomputer graphics and image processing it is mostly usedHSVcolor model since it can

be easier to pick out range of colors than in RGB by human [6]. It consists of three

componentshue, saturation and value. Due to reasons we have used HSV.

2.5 Detection algorithm

Before describing the algorithm we would like to note, that values of thresholds

was drawn and designed for laser pointers with laser power below 1mW. Our testing

devices were laser pointers Logitech Professional Presenter R800 (green laser) and

Legamaster LX-1(red laser).

At beginning an input image from camera is captured and converted fromRGBto

HSV color model. Our interest is focused on value channel from HSV of the inputimage. If the camera exposure is correctly configured, color of laser spot will display

brightest color in the input image. Then average value of value channel pixel isacquired (in OpenCV is this value in interval 0-255). It is necessary to configure

camera exposure if average value is too high because image is too bright and laser spot

may not to be recognized. The average value is compared to AIV1 threshold. If it is

bigger than the threshold then exposition is adjusted. The AIVthreshold is set to 30%of value channel maximum value (255*0.3 ~= 77). Big value of AIV threshold can

1

AIV thresholdaverage input value threshold

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cause many of false detections or unwanted merging of different groups with high

brightness into big ones. On the other side, very small value causes unfulfilled

requirements on exposure or loss of demanded brightness sources.

After the camera exposure correction is carried out the highest value from value

channel is acquired andDV2threshold is determined. TheDVthreshold is dynamically

computed for each input image. Value of DV is set to 95% of highest value of value

channel of current image so the rest in 5% contains bigger group of valid pixels, which

can be possibly our laser spot.

Number of pixels that fitDVthreshold is found. If the number is bigger than 100,

the algorithm skips further recognition steps to maintain real-time computing. This also

may mean a change of light condition during detection and it is necessary to correct the

exposure again. We found out during testing, that average size of one laser spot, is

presented by group of pixels with high value 36 (6x6) in valuechannel. If the numberis below limit, pixels selected byDVthreshold are grouped. Pixels in group are explicit

neighbors; also group may consist of one pixel. Then for each group centroid and also

diameter is computed as follows:

where n is number of pixels in a group. Result of root is ceil-rounded to nearest

high integer number, so diameter value is always at least 1. The reason for using the

square root is assumption that a shape of laser spot is circle (only nearest surroundingis selected) and cropped area is square. For further calculation is provided on square

cropped area with length of side 2x diameter + 13.

Next step is obtaining the highest value from valuechannel of cropped area. New

threshold is constructed and its value is set to 90% of this highest value. To determinewhether the cropped area contains laser spot is realized by the proportion of area size

and count of pixels from area that fits the threshold (value may not be bigger than 0.3).

Color recognition is last step of the algorithm and this process searches for pixels

with color within defined intervals in cropped area (it is possible do detect multiple

colors and detect continually multiple laser spots). The resulting color is the one that

has highest number of pixels as first.

The algorithm can now be described as follows:

1.

Capture image from the camera.

2.

Convert image from RGB to HSV color model.

3.

Check the exposition of camera. If it is not suitable make corrections and

go to the step 2.

2Dynamic value threshold

3

The 1 is width of centroid.

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4.

Obtain maximum value of valuechannel and calculateDVthreshold.

5.

Obtain number of pixels that fitDVthreshold. If the count > 100 go to

the step 2.

6.

Group pixels that fitDVthreshold.

7.

For each group do:

a.

Compute diameter and centroid.

b. If the proportion of cropped area size and cropped area

threshold > 0.3 get next group.

c. Recognize laser spot color and add data to the local list.

8. Update the global laser spot list from the local list.

9. Go to the step 1.

2.6

Localize the position of laser spotWe can detect a position of laser spot in an image captured by a camera. Since a

camera can be located in different positions in the scene the captured image can be

warped. In order to use laser pointer as a mouse we need to transform coordinates from

surface of projection screen in image captured by a camera to coordinates on screen in

computer. Therefore we create a transformation matrix using a calibration image of

chessboard. The transformation matrix is computed by known points in tiles on screen

of computer and same detected points from image captured by a camera. For this

purpose finding homography is used with Least-Median robust method.

2.7

Complexity proposed algorithmAverage processing time takes 62.5ms (16 frames per second) that means it could

be used in real-time processing. The computing time of the laser detection algorithm

can be improved by rewriting some parts of code to C programming language.

3 DEMONSTRATION GAME

To demonstrate the functionality of our proposed method we have developed a

simple game inspired by the Duck Hunt game. The main goal of the game is to shoot a

flying duck using laser pointers.

The game works as follows (Fig. 2). A computer displays the image from desktopscreen of the game through a projector on a projection screen. The projection screen is

captured by a camera which is adjusted to proper exposition setting so the detection

algorithm can recognize laser spot if one use laser pointer.

At first we have to establish coordinates of projection screen using calibration

image of chessboard. If laser spot appears on the projection screen, the algorithm

detects it and transforms the coordinate of it to computer coordinates. If the coordinate

is overlapped with a duck, the duck is hit. In addition the sound of shot will be played.

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The game can be playable by two players, since our detector can detect two colors

of laser beamred and green. Every shot is counted for red or green player, so playerwith highest score is winner.

The Duck Hunt game was presented at Open Day of Faculty of Management

Science in ilina, Prievidza, and at Prien 2013 organized by Bilingual GrammarSchool in order to demonstrate it and stimulate potential students for research area.

Everyone could play the game right after take a laser pointer into hands.

Figure 2 Scheme oflaser spot detection for the demonstration game

4

CONCLUSIONThis paper proposed the method for detection of the laser spot emitting by a laser

pointer on the projection screen. The algorithm is based on the camera settings and

value channel of HSV color model. It can recognize more laser spot colors at once.

The algorithm works very well. The developed game proves useful of the

algorithm as it has been used for several times to demonstrate of computer vision area.

Usage of laser detection algorithm is not only for games but for example to control of

presentation through laser pointer (drawing with pointer on a projection screen),

computer control or commercial use in entertainment industry. Therefore our future

work will be focused to optimize the recognition algorithm and then to create a real-

time application for computer control by gesture.

ProjectorProjected image on

projection screen

Source image

Computer

Captured image

Camera

Laser pointer

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Acknowledgement

This contribution/publication is the result of the project implementation:Centre of excellence for systems and services of intelligent transport II.,

ITMS 26220120050 supported by the Research & Development Operational

Programme funded by the ERDF.

"Podporujeme vskumn aktivity na Slovensku/Projekt je spolufinancovan zo zdrojovE"

REFERENCES

[1] MIKAWA, M., MORIMOTO, Y., TANAKA, K.: Guidance method using laser

pointer and gestures for librarian robot, IEEE RO-MAN, Sept. 2010

[2] SOETEDJO, A., NURCAHYO, E., NAKHODA, Y. I.: Development of a cost-

effective shooting simulator using laser pointer, International Conference onElectrical Engineering and Informatics (ICEEI), July 2011

[3] WADA, T., TAKAHASHI, M., KAGAWA, K., OHTA, J.: Laser pointer as a

mouse, Annual Conference SICE, Sept. 2007[4] CHAVEZ, F., FERNANDEZ, F., GACTO, M. J., ALCALA, R.: Automatic

Laser Pointer Detection Algorithm for Environment Control Device Systems

Based on Template Matching and Genetic Tuning of Fuzzy Rule-Based

Systems, International Journal of Computational Intelligence Systems, Vol. 5,

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[5] BYUNG, M. J., NGYUEN, D. D., SANG J. L., JUNG D. J., JAE W. J.:

Hardware architecture for detecting laser point using FPGA, 12th InternationalConference on Control, Automation and Systems (ICCAS), Oct. 2012

[6] WEN, C., SHI, Y.Q., GUORONG X.: Identifying Computer Graphics using

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International Conference on Multimedia and Expo, July 2007

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