MULTILIGHTTRACKER: VISION BASED MULTI OBJECT

TRACKING ON SEMI-TRANSPARENT SURFACES

Jesper Nielsen, Kaj Grønbæk

Interactivespaces, Dept. of Computer Science, University of Aarhus, Denmark

Keywords: 2D object tracking, computer vision, color tracking, multi-user, gesture, tangible, multi-pen interaction,

interactive surface.

Abstract: This paper describes MultiLightTracker (MLT) - a simple and robust system for simultaneous tracking of

multiple objects on 2D semi-transparent surfaces. We describe how the system performs object tracking on

a surface which can be simultaneously used for back projection, allowing direct and undisturbing single- or

multi-user interaction with the projected content. The system is vision based, supporting both 4:3 and 16:9

picture formats, and it requires only a webcam and a recent PC to work. MultiLightTracker currently tracks

four different objects simultaneously in real time (~100ms latency) but the aim is to extend this number. In

controlled environments such as meeting rooms or living rooms, MultiLightTracker is sufficiently robust for

everyday collaborative use. Thus, MultiLightTracker is superior to existing multi-object tracking surfaces

with regards to its easy availability, simplicity and comparable low cost.

1 INTRODUCTION

Many computer applications supporting co-located

collaboration require simultaneous manipulation of

digital objects on 2D surfaces such as interactive

boards, screens or tables. A simple example is the 4-

user drawing-table application in Figure 1. At

Interactivespaces we have developed several design

concepts for homes, schools and workplaces

supporting multi-user and social interaction around

2D surfaces such as tables, walls and whiteboards. A

recurring problem to us in this context has been to

find easily available and inexpensive hardware that

supports multi-user interaction - preferably on back-

projected surfaces as these are much more

convenient when several users are involved, due to

the higher risk of casting shadows. Research

prototypes such as DiamondTouch (Dietz, 2001),

and SenseTable (Patten, 2001) can track many

objects simultaneously but suffer from complexity

and lack of availability as well as the use of front

projection. Commercial products like DViT from

Smart Technologies can track 2 fingers or pens

simultaneously, but are expensive and cannot

discriminate valid objects from other (non-valid)

objects on the surface. Thus, multi-object tracking

systems of today are not yet easily accessible to

application developers.

Figure 1: MultiLightTracker used with a simple

simultaneous 4-user drawing application.

In this paper we present MultiLightTracker

(MLT), a novel system that realizes 2D position

tracking of a number of simple objects on a planar

semi-transparent surface without using custom or

expensive hardware or software – only standard

Light Emitting Diodes (LEDs), a camera and a

recent PC is used. The tracking software is based on

EyesWeb (EyesWeb, 2005), a freely available and

easy-to-use signal processing application, containing

245

Nielsen J. and Grønbæk K. (2006).

MULTILIGHTTRACKER: VISION BASED MULTI OBJECT TRACKING ON SEMI-TRANSPARENT SURFACES.

In Proceedings of the First International Conference on Computer Vision Theory and Applications, pages 245-252

DOI: 10.5220/0001374002450252

 SciTePress

predefined function blocks for different data types

and -operations.

To sum up, the key features of this setup are the

following :

• the tracking system is based on use of a semi-

transparent surface and supports simultaneous

back-projection, producing an environment very

suitable for multi-user interaction.

• the hardware setup is physically robust as it is

placed behind the interaction surface - out of

reach from the users.

• the used hardware is off-the-shelf and

affordable; we use Universal Serial Bus (USB)

webcams or DV cams (IEEE1394), Light

Emitting Diodes (LEDs) and a recent PC.

• the vision tracking- and network software used

is freely available for non-commercial purposes.

The paper is structured as follows: First we

describe the hardware setup, next we describe the

MLT software. This is followed by a performance

study, then we discuss challenges, application

scenarios and related work. Finally we discuss future

work and conclude the paper.

2 HARDWARE SETUP

We focus on the implementation of a computer

vision system that processes the video stream of a

single video camera. We equip each of the objects to

be tracked with one or more uniquely colored high-

intensity LEDs, enabling us to detect and track the

colored light spots with the camera.

Figure 2: Example of a vertical MLT setup.

An example MLT setup is shown in Figure 2 where

(2) is a semi-transparent surface – a sheet of glass

with either a sand blasted back side or a matte film

can work, but a professional low-gain back-

projection screen is optimal for exploiting the full

potential of the system and will also greatly improve

image quality when using back-projection (4). It is

important that the camera (3) and projector are both

placed behind the tracking surface to avoid shadows

from users and camera overload due to direct

projector light exposure. The camera is connected to

a PC (not shown). The system tracks the light from

one or more uniquely colored LEDs embedded in

each object (1). The object can be in form of a pen, a

puck or another shape - see Figure 3 for examples.

The setup may be angled arbitrarily from vertical to

horizontal and common interaction techniques such

as single / double click and gesture tracking may be

supported for multiple objects simultaneously.

Figure 3: Interaction objects. Left: pen for normal 2D

interaction. Right: “donut” with 2 different-colored LEDs

allowing additional rotation sensing.

2.1 Multiple Object tracking

dependencies

Successful tracking of multiple objects in MLT

depends strongly on the systems ability to always

clearly and exclusively see, and uniquely

distinguish, the different LED colors. The maximum

number of tracked objects is thus constrained by

these factors :

• The ability to effectively control the camera

gain and shutter, keeping unintended light – i.e.

ambient light and back-reflection of the projected

image - below the camera threshold.

• The available LED colors and their spectral

distance (see Table 1) as well as sufficient (and

stable) LED light output to overcome the camera

threshold without overloading the camera.

• The system’s color stability over the whole

surface. A combination of the camera’s color

resolution and the color distortion of lenses and

eventual mirrors.

Currently the system supports 4 simultaneous

colors but ongoing work is aiming to extend this

number. With 4 colors the system easily supports

two-handed and/or multi-user interaction as well as

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objects with more than one LED embedded. This

can enable the orientation of the object to be

detected (see Figure 3).

2.2 Back projection

MLT can be used without projection but a rear

mounted projector (Figure 1 item 4) can be used to

provide graphical feedback directly on the surface.

MLT is able to track the LED-equipped objects

regardless of the presence of a back-projected

image, provided the back-reflection from the

diffusion layer is not too strong. This can be

minimized by reducing projector intensity and by

using a diffusion layer with a low gain factor.

Apart from being required for back-projection,

the diffusion layer helps ensure the camera will

always see the LED light more evenly. Many LEDs

have a very narrow angle of output (< 20 deg.)

which can pose problems when the LED is not

facing the camera lens directly. This can happen

when the user tilts the LED or when the LED is used

near the edge of the surface.

2.3 Use of mirrors

In many back projection setups it is necessary to

apply mirrors in order to achieve projections of the

desired size with minimum space requirements.

MLT works well with mirrors but experiments show

that ghost images, produced by the glass layer in

front of the mirroring surface of ordinary mirrors,

can degrade the system’s color separation and

thereby disturb tracking stability and accuracy.

Optimal performance from the projector and the

vision based tracking requires use of front-coated

mirrors, as these do not distort the picture in a

similar way.

2.4 Camera calibration

With normal ambient light and an eventual back-

projection turned on with a high brightness (white)

picture, place the camera so it covers the back of the

chosen interaction surface. Set the camera to manual

shutter speed and gain control. Adjust the gain to

zero and increase shutter speed until nothing is seen

in the picture (approx. 1/1000 sec).

Note that the system will not tolerate strong light

sources such as unscreened lamps or direct sunlight

to hit the interaction surface.

2.5 LED color selection

When selecting LEDs for MLT, choose high-

efficiency colored LEDs with outputs > 3000 mCd

and wavelengths as far apart as you can get. For a

selection of LED colors and their typical spectral

wavelengths, see Table 1.

Table 1: Different LED colors and their wavelengths.

Color Wavelength [nm]

Pink 440

Blue 470

Turquoise 490

Cyan 505

True green 525

Green 570

Yellow 590

Orange 605

Red I 630

Red II 660

2.6 LED intensity

As LED intensity varies a lot with model and color,

it is necessary to adjust the different LED intensities

to suit the camera, which will have limited dynamic

headroom now that its auto-shutter and auto-gain

functions are disabled.

LED intensity is tested by first pointing the

chosen LEDs into the interaction surface while

driven at their specified maximum current (typ. 20

mA - consult the data sheet). Locate the weakest

LED still clearly visible in the camera picture. Any

LEDs weaker than this one must either be discarded

or replaced with brighter LEDs. Reduce the intensity

of brighter LEDs (by reducing the current) until all

LEDs show up evenly bright in the camera picture.

Decreasing battery voltage means decreasing

LED intensity. If you are using batteries, you can

either change these often or use a constant-voltage

circuit to drive the LEDs. The description of such a

circuit is, however, beyond the scope of this article.

3 MLT SOFTWARE

ARCHITECTURE

The MLT utilizes computer vision- and color

detection techniques based on the EyesWeb

framework. In Figure 4 a video camera captures

images to a PC. On the PC the camera signal first

enters the camera driver. This is where a manual

sensitivity level is set to match the light conditions

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during setup (note that most DVcams have these

controls built into the camera instead).

The video signal then enters the EyesWeb

application which tracks the 2D positions of preset

color blobs in real time. The positions of the tracked

blobs are transmitted from EyesWeb as Open Sound

Control (OSC) (OSC, 2005) messages on a network

socket.

Finally, we are using a TCP server program to

transform the OSC messages into eXtensible

Markup Language (XML) messages. The XML

messages are available on a network socket and can

easily be used by application programmers, i.e. for a

Graphical User Interface (GUI) back-projected onto

the 2D surface.

As shown, a GUI application can run on a

separate machine (illustrated by the dashed line) or

locally on the same machine as MLT if there is

enough processing power available for this.

3.1 The EyesWeb image processing

algorithm

The color tracking is based on EyesWeb which

provides a graphical programming environment

containing predefined function blocks for different

data types and -operations. See Figure 5 for

algorithm architecture.

We have used a multi color tracking block for

video (ExtractMultColors) followed by some data

formatting functions and an OSC network socket

block to output the result of the tracking process.

Going from left to right, the first block is a video

display used to verify the camera input signal. The X

indicates the block is inactive and the blue signal

lines indicates that the signal type is image data.

The next block is the camera input, which can be

either a webcam (shown) or a DVcam input. The

camera feeds two blocks on the right – a (disabled)

display used when calibrating the color tracker

and the color tracker (ExtractMultColors) itself.

The color tracker block can, as its real name

implies, extract multiple colors but we only use it to

track one color as this makes the setup easier to

handle and has shown to give no performance

penalties. To track additional colors we duplicate the

whole setup from the color tracker block onwards,

feeding the camera signal to the added signal chain.

3.2 Calibrating the color tracker

Before calibration is possible, it is necessary to open

the color tracker by double-clicking it and manually

add an entry to the tracking list inside. The entry

includes a name for the blob (which can be the color

name) as well as the area size to search. As you can

never know where a LED will light up and as the

color tracker searches an area centered around the

last occurrence of a color, it is necessary to enter

search dimensions twice the size of the camera’s

resolution to always scan the whole surface.

Above the color tracking block there is a small

block with a tick-box. Once the EyesWeb algorithm

is running, this tick box can be activated along with

the display to the left of the color tracker. It is now

possible to calibrate the color tracker by activating a

LED on the interaction surface and simply double-

click the corresponding color dot in the display.

When calibration is done, close the display and un-

tick the box. This procedure must be repeated for

any additional colors.

3.3 Output processing

The color tracker has several outputs on its right

side, of which 2 is used. At the top is an image

output showing what the color tracker recognizes.

The other output is coordinate data (pink signal).

Figure 4: MLT software architecture. The system can run on a single PC if resources allow it, or be distributed on two

machines (dashed line).

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The data is shown on-screen by the block with the

“spreadsheet” pattern and contains the name you

entered inside the color tracker along with

coordinates of the color blob. Note that both

coordinate outputs will yield a large negative

number if no color is recognized.

The matrix data is also fed to two rows of blocks

that extract the X- and Y-coordinates, convert the

data from scalar to string and merge the strings with

preset text labels (the VALUE field in Listing 1)

before transmitting the strings in OSC messages. It

is necessary to set a network port in the OSC blocks.

3.4 Communicating data to

applications

The output from EyesWeb is OSC messages but we

have chosen to convert this to XML with the Flosc

server application (Flosc, 2005). This freely

available application is written in Java and includes

full source code, making it easy to make

modifications. For MLT we modified Flosc to only

output XML messages when the received

coordinates are valid (suppressing the large negative

numbers output by EyesWeb when no color is seen).

We also made some modifications to the output

XML format to reduce bandwidth. Here is an

example of the XML output for a blue color X-

coordinate – note that X and Y coordinates are

currently output as separate XML messages :

<XML>

</OSCPACKET>

</XML>

3.5 Calibrating output coordinates

to the display

No attention has been paid to ensure that the

coordinates coming out of the color tracker matches

the coordinates of the displayed image. This means

that a calibration must be performed before the

system is used. A typical calibration procedure will

project markers in different locations of the screen

and the user must activate a LED over each of these

markers in turn. The system will then detect the

resulting camera coordinates and introduce the

necessary correction. This method can also detect

and correct geometric distortion as a result of the

camera not being placed correctly (skew /

keystoning). Additionally, camera coordinates can

be flipped / mirrored if this was not possible on the

camera or in the camera driver.

As MLT is currently a sensor system outputting

raw data, calibration and data-interpreting tasks are

left to the application receiving the XML data.

3.6 Click functionality

If the LED-equipped objects have a suitable switch

function, either the Flosc application or the

application receiving the XML data can emulate

mouse single- and double-clicking functionality by

measuring the amount of time the LED is turned on

and off.

4 SYSTEM PERFORMANCE

System performance depends on camera resolution,

camera frame rate and the number of colors tracked

by the MLT application. The current version of the

system is running on a Dell Dimension 8400 P4 3,4

GHz with 1GB RAM using a Logitech Quickcam

Pro 4000. All measurements have been performed

on this system configuration.

4.1 System load and response time

Table 2 shows the average tracking latencies and

corresponding system loads for a 640x480 setup

tracking 1 to 4 colors. The latency and load figures

Figure 5: EyesWeb algorithm architecture for tracking a single color.

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tend to fluctuate a bit during measurements, so 10

measurements were made and averaging has been

applied.

Table 2 : System performance.

Colors Latency (avg) System load (avg)

1 70 ms 22 %

2 80 ms 33 %

3 90 ms 42 %

4 100 ms 55 %

4.2 Tracking resolution

With a webcam the normal resolutions are 320x240

or 640x480 in 4:3 aspect ratio. With an IEEE1394

DVcam the resolution is 720x540 for PAL 4:3 or

720x405 for PAL 16:9 (most DVcams make 16:9 by

cropping the image).

Tracking 4 colors from a webcam (640x480 @

15fps) and running the additional network socket

loads the hardware between 50 and 60%. In

comparison the same number of colors can be

tracked in 320x240 resolution at only 15% load.

Clearly, reducing the camera resolution also

reduces the load on the processor as the number of

pixels to search fall dramatically. Reducing the

number of colors to look for also reduces processor

load, but not nearly as dramatically. This is probably

because EyesWeb optimizes the algorithm before

executing it and looking for an extra color isn’t a

very demanding task when looking for one in the

first place.

Opting for lower resolution images means you

get lower tracking resolution. Today, projector

resolution is often 1024x768 and webcams will

normally deliver 640x480, resulting in (maximum)

1.6 display pixels per tracked camera pixel. This

does not pose problems with normal window

interaction but is noticeable to some degree in a

drawing situation. DVcams will deliver a bit more

resolution, reducing the max. displayed/tracked pixel

factor to 1.42.

4.3 Frame rate

Reducing the frame rate reduces the load

accordingly. As the camera frame rate is already

low, this will easily become uncomfortable. Tests

show that below 10fps the staggering effect becomes

noticeable when drawing or moving objects.

5 DESIGN ISSUES AND

CHALLENGES

Most camera based systems are sensitive to changes

in light conditions and MLT is no exception. Its

stability depends highly on the effective suppression

of unintended light and the stability of the LED light

sources’ color and intensity. As long as these factors

are well-adjusted and stable, the system will work.

The optical system’s resolution of the color

spectrum puts a limit to the number of simultaneous

objects. Currently we can easily track 4 colors and 6

seems to be realistic but if more objects is desired,

other techniques must be used to distinguish them.

Due to camera- and projector-screen distance

increasing with surface size, MLT setups can

become quite space demanding. This is a limiting

factor for the system’s applicability.

Improvements can be made to the EyesWeb

patch by rewriting it to take over some of the

functionality currently performed by the Flosc

application. This will improve performance as a lot

of unnecessary information is currently being

generated, just to be filtered away in the Flosc

server.

6 APPLICATIONS

The MLT technology has recently been proofed and

tried out for a three week period in a public

installation at a library. Results are very promising,

indicating that the system is sufficiently stable and

robust for use in a public space, although some

testing still remains to reveal its limits. Moreover, it

is planned to use the technology in a number of

future multi-object/multi-user application scenarios,

examples are described below.

Before showing the system in public, it was

decided by the University of Aarhus to apply for a

patent on the technology.

6.1 StorySurfer

In our first example, MLT is used as part of a large

scale interactive space installation - a multi-user

search tool for a children’s interactive library project

at the Main Municipal Library in Aarhus, Denmark.

The setup includes a large camera-tracked, top-

projected floor where the children can select book

categories by keywords and pick out single items by

for further investigation. The selected book cover

objects are transferred to a horizontal MLT table

where up to 4 users can simultaneously browse and

discuss the details of the selected books and print out

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a slip with directions on how to find the actual

books.

Figure 6 shows the user interface. The book

representation is maximized and moved around the

screen by dragging the hand symbol. Clicking the 4

symbols on the left give access to book facts, book

abstract, a “other users also looked at ...”-function

and a print function.

The children’s interactive library project has not

yet finished and additional research results will

emerge from it during 2006.

Figure 6: The user interface of the StorySurfer table. Note

the high image quality and even light of the low-gain back

projection screen.

6.2 DoHM

Another application example is the interactive home

environment where we have developed a so-called

Domestic HyperMedia system (DoHM) (Petersen,

2004), enabling family members to collaboratively

organize home media material such as pictures,

videos, play lists etc. on the living room table or an

electronic pin board in the entrance hall. With the

MLT it is now easy to transform DoHM clients into

true collaborative applications giving all family

members equal control over the digital material

placed on the living room table, thus supporting the

kind of accessibility and social interaction that was

required from our empirical studies in the domain.

Having finalized the first stand alone testing of

the MLT we are now integrating it in the

applications mentioned here and perform user

evaluation. The MLT is as a first step integrated into

the DoHM MediaOrganizer client.

7 RELATED WORK

The MLT work is aimed at creating a low-level,

event-based input device for different interactive

systems that support single or multiple users

interacting with a single surface. Examples of such

systems are DiamondTouch (Dietz, 2001),

Lumisight Table (Matsushita, 2004), MagicTable

(Bérard, 2005), MetaDESK (Ullmer, 1997) and

Sensetable (Patten, 2001). Compared to these

systems we have developed a significantly simpler

and cheaper hardware setup.

Some of the hardware components, such as the

projector and PC, are standard equipment that is

needed in any setup of this kind. This means

expenses must cover the LED-equipped objects

(they require a little crafting), a webcam and a low-

gain back projection screen. This should not set you

back more than 500-1000 EUR/US$ for a 4 color

setup with a 100-150 cm. screen (diagonal).Front

coated mirrors are expensive – 1000-1500 EUR/US$

per sq.m. but can often do without them - screens

and mirrors can be purchased from companies like

(DNP, 2005) or (DAF, 2005).

By using back-projection and –detection we get

high image quality without shadows and completely

avoid the problems with occlusion that other systems

like ARToolKit (Kato, 2000) have to deal with.

With respect to software, MLT provides a

generic XML based interface over a TCP/IP port

which makes it very easy for application

programmers to prototype and develop client

programs that take advantage of the LED object

tracking. As an example, Macromedia Flash can

read the XML stream directly and makes it easy to

couple tracking of the physical LED objects to

graphical objects in Flash. The socket-based

architecture also supports multi-client setups where

one MLT system can supply multiple client

applications with XML encoded object position data.

Thus, the MLT is easier to integrate with arbitrary

applications than previous systems.

8 FUTURE WORK

In the following we discuss a number of issues and

challenges to address in the future development of

MLT.

Testing of the applicability of RGB (tricolor)

LEDs with MLT has already started as these LEDs

might enable more colors to be used simultaneously

by providing custom colors not available within the

standard LED product range.

Development of a custom EyesWeb processing

block, integrating data filtering, formatting and

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direct XML output to a network socket will simplify

the architecture of the MLT and reduce both

processing load and tracking latency. This process

could further lead to the development of a single

standalone image processing application, containing

all the software processes of the MLT system.

Development of a multi-projector and multi-

camera setup for large surfaces will allow whole

walls or floors to be turned into multiuser interaction

surfaces. This will require further development of

the Flosc server, as this will initially be the point

where data streams from several Eyesweb color

trackers are joined together.

9 CONCLUSION

This paper has introduced the MultiLightTracker

(MLT) system for simultaneous vision based

tracking of multiple objects on semi-transparent 2D

surfaces. We have described the object tracking

approach and how it enables direct multi-user

interaction with back projected content.

MultiLightTracker has initially been calibrated to

track four different-colored objects simultaneously

but it will extend to track additional simultaneous

objects. MultiLightTracker is sufficiently robust for

everyday collaborative use and is superior to

existing multi-object tracking surfaces with regards

to its simplicity and low cost. We have reached a

stage where MultiLightTracker is now being

integrated with applications for home and public

environments, leading to evaluations in real use

situations.

10 ACKNOWLEDGEMENTS

The authors wish to thank all colleagues at

Interactivespaces for their help and support with the

development of MLT and this paper.

Special thanks goes to architects Andreas Lykke-

Olesen and Tina Christensen for contributing with

photos and illustrations to the project.

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