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ROBOT - ROBOT Orange Ball-shaped Object Tracker

An algorithm to detect the ball for RoboCup-Junior Soccer.


Color Based

HSV colorspace

The module converts the BGR (Blue-Green-Red) image to a HSV (Hue-Saturation-Value). The HSV colorspace has the huge advantage that only the Hue and Saturation values need to be precise. The value (comparable to the brightness), does not need to be checked so the filter shouldn't be influenced by changing brightness.

The algorithm blurs the image at first to reduce noise. Next it checks pixel-wise if the respective pixel fits the criteria (minHue, maxHue, minSaturation...). If it fits it will be drawn as a white pixel on the output img, if not it will be drawn as a black image.

Finally all contour areas get sorted by size and the largest is highlighted in the image.


The Canny algorithm is used to detect edges in the image. The algorithm only works on a single color plane (for example Black and White), that's why we apply a special colour filter beforehand. The contours then are checked on their circularity.

Colour Filter

The colour filter was designed to get the best possible contrast between the ball and its surroundings. Therefore we use two filters:

  • The first one is a simple red filter, changing objects with high green component to dark and bright or reddish objects to white. This however results in the Canny algorithm being unable to clearly distinguish between white lines and the ball. When the ball lies on said lines, its contours might melt together with the lines' ones. To avoid this problem we use a second filter.
  • The second filter is a simple green filter. After applying this filter to the original image the field and the line appear white and the ball completely black. We additionally invert the filtered image so the ball becomes a white object while the field and other objects become dark.

Both images are combined, but with different weight. This results in an overall grayish image where only orange / red objects are clearly visible.

Canny algorithm

The algorithm uses gradient based edge detection to find possible edges in an image. It basically consists of following procedures:

  1. A Gaussian blur is applied to smooth the image in order to reduce noise
  2. Calculate the intensity gradients of pixels
  3. Suppress maximums close to larger intensity gradient maximums
  4. Apply double threshold to classify edges in strong, weak edges. Edges below both thresholds will be ignored
  5. Edge tracking: Ignore weak edges which are not connected to strong ones.

For further information on the Canny algorithm see:

CircleFinder submodule

The CircleFinder submodule determines which objects created by the Canny algorithm resemble a circle-like object. Every Canny-object will be put through following procedures:

  1. If an object contains less than a specific amount of points (e.g. a line which only has a start and an end or a small noise dot), it will be determined a non-circle object.
  2. The algorithm places a triangle with two equally long edges in the object.
  3. If the triangle is to flat (two angles are smaller than a specific threshold), the object will be determined a non-circle object.
  4. For each triangle edge the midperpendicular is calculated.
  5. The intersections of said midperpendiculars are calculated. If there is no intersection, the object is determined a non-circle object.
  6. Since more than one intersection of the midperpendiculars can exist (due to inaccuracy during measurement or calculation), the middle of all intersections is calculated. If however the intersections differ to far from each other, the object will be determined an non-cirlce object.
  7. The middle of the three intersections resembles the center of a potential circle.
  8. The radius of the potential circle is calculated as the average of the distances of each triangle corner to the center.
  9. The distance of every point of the object to the center is calculated. If said distance differs to far from the radius calculated in step 9, the object will be determined a non-circle object. The difference-threshold used in this step to determine whether an object is a circle is calculated according to the potential circle's radius.
  10. If all previous steps are passed, the object will be determined a circle-like object.


CircleFinder Module

Calibration during runtime

There are three sliders which let you configure the canny threshold and the colour filter:

  1. Threshold: Sets the threshold for the edge detector. A high value means that only prominent and clearly visible edges will be detected by the Canny algorithm. At a low value the edge detector is more tolerant.
  2. Color bias: Determines the ratio of the two pre-color-filters. Low values (slider on the right) mean that the inverted green filter will be weighted greater than the red filter and vise versa.
  3. Contrast: Sets the contrast of the filtered image. Default value is 20

Example how to configure the three values to ensure the best results:

  1. Set Threshold and Color bias to maximum and Contrast to default value.

    Image Step 1

  2. Set Color bias to a value where all background colors appear in the same grayish tone and only reddish colors are clearly distinguished.

    Image Step 2

  3. Adjust the contrast so reddish areas are marked by red / green lines (detected as edge). You can do this simultaneously with the previous step if necessary.

    Image Step 3

  4. Reduce the Canny threshold to improve edge detection of reddish objects. Don't go to low to avoid noise.

    Image Step 4

    or (with a lower contrast, the canny threshold needs to be lower. Both setups work equally well. Choose one depending on the environmental lighting)

    Image Step 4 alternative

Configure the circle detection

Following variables let you configure the CircleFinder module:

  • minimumPoints: Edge-objects consisting of less points than specified will be ignored without even being put through the whole test procedure. You can use this variable to reduce the amount of objects the CircleFinder has to go through. A recommended value would be around 10.

  • minAngle: The algorithm tries to fit triangles into detected objects which are used to calculate the center of a circle later. Objects which are to flat or long are sorted out by checking the angles of the corresponding triangle. The higher this parameter is, the bigger the volume of an object has to be to pass this test step (flat objects can only contain flat triangles). The default value is 30.

  • maxRadius and minRadius: Round edges with a radius not between max and minRadius will be sorted out. Use this option to ignore small noise dots or large slightly rounded edges which don't resemble a circle.

  • circleCenterDistanceThreshold: When calculating the circle of a potential circle, the algorithm first determines three center candidates which theoretically are all the same. Due to inaccuracies the three points slightly differ. This parameter restricts the allowed distance (in pixels) between said points. Normally, the default value of 10 should do the job.

  • triangleLineLengthRatioThreshold: If possible the algorithm tries to fit a triangle with two equally long sides into an object. This parameter determines the maximum difference between said triangle sides (1 = 100%, 0.5 = 50%...). A higher value means stricter criteria for the circle detection. Normally, the default value of 0.1 is recommended. Changing the value drastically can result in bad detections.

  • max-/minRadiusRatioTolerance: At different distances between the camera and the object, the algorithm has to be more or less tolerant in terms of the ratio between the theoretical calculated radius of the object and the actual distance between the objects' points and the potential center. This value sets maximum / minimum tolerance for executing said procedure.

  • radiusRatioToleranceFactor: The tolerance for comparing the distance between an object's potential center and its points to its calculated radius is inversely proportional to the object's size (its calculated radius; the smaller the object, the more tolerant the algorithm). This parameter determines is the proportional factor used for the scaling. At high values the tolerance will decrease more rapidly from small to large objects.

    NOTE: The calculated tolerance will always be between max- and minRadiusRatioTolerance.

Step-by-Step instruction
  1. Calibrate the color filter so that the object you want to detect is clearly visible and distinguished from the background.
  2. Set all parameters with default values to their default values (minimumPoints, minAngle, circleCenterDistanceThreshold, triangleLineLengthRatioThreshold).
  3. Find out what the largest and smallest possible circle will be and set the corresponding parameters to that value (maxRadius and minRadius, in pixel!).
  4. Set radiusRatioToleranceFactor and maxRadiusRatioTolerance to 0.
  5. Set minRadiusRatioTolerance to a value where the object (the ball) is only detected as circle (marked green) on close range (about 10-20 cm, depends on your camera). A recommended value would be between 0.1 and 1. Note the value and set it back to 0.
  6. Set maxRadiusRatioTolerance to a value where the object (the ball) is only detected as circle (marked green) on maximum range. A recommended value would be between 0.1 and 1.
  7. Set minRadiusRatioTolerance back to the value you used in step 5.
  8. Start with radiusRatioToleranceFactor at 0.1 and double or halve the value until the ball is detected as circle at every distance (marked green). Keep the value as high as possible to avoid false-positives!
  • Reduce noise and too small objects by adjusting minimumPoints.
  • Avoid false-positives by:
    • Adjusting max- and minRadius
    • Setting minAngle to a higher value
    • Lowering max-/minRadiusRatioTolerance
    • Setting radiusRatioToleranceFactor to a higher value
    • Setting triangleLineLengthRatioThreshold to a lower value
  • Bad detection of far away or small objects:
    • Set minRadius to a lower value
    • Set maxRadiusRatioTolerance to a higher value
    • Set radiusRatioToleranceFactor to a lower value
    • Set minAngle to a lower value
    • (Set triangleLineLengthRatioThreshold to a higher value)
  • Bad detection of close and large objects:
    • Set maxRadius to a higher value
    • Set minRadiusRatioTolerance to a higher value
    • Set radiusRatioToleranceFactor to a lower value
    • Set minAngle to a lower value
    • (Set triangleLineLengthRatioThreshold to a higher value)
  • Bad detection of objects at a medium distance:
    • Adjust radiusRatioToleranceFactor

Coding Style

The code style should follow the code style The only difference is the naming convention: we strictly advise you to use camelCase naming, this helps the readability and reduces the length of variable names.


Main Toolchain

  • OpenCV (3.1)
  • g++ (5.4.0)
  • CMake (3.6)
  • Doxygen (1.8.11)
  • Google-Test (1.8.0)
  • cppcheck (1.72)

Setup the development tools

At the moment Ubuntu (16.04 amd64) is the only supported OS. The code can run on all *nix system but hasn't been tested.

First install the compiler, cmake (the build-system), doxygen (used for documentation), git (used as VCS) and cppcheck (used for static code analysis):

sudo apt-get install build-essential cmake doxygen git cppcheck


Next you have to compile OpenCV: First install all dependencies:

sudo apt-get install libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev

Create a new folder in your working directory (for example ~/OpenCVInstallation). In the Terminal change directory to folder (cd ~/OpenCVInstallation). Then run:

git clone

Next compile it, therefore create a build directory:

Change to repo folder:

cd opencv

Create a new folder:

mkdir build

Change to build folder:

cd build

Next run CMake to configure your installation:

cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local ..

If there is no error, compile OpenCV, this may take some time:


Finally, install the compiled OpenCV:

sudo make install


To do unit-testing you need to install GTest.

Download the source and install the dependencies:

sudo apt-get install libgtest-dev

Next change to source-directoy:

cd /usr/src/gtest

Next run CMake to configure the project:

sudo cmake CMakeLists.txt

Compile the project:

sudo make

Finally install GTest:

sudo cp *.a /usr/lib

Installation on the target device

Build tools for the target Device

Install the compiler, cmake (the build-system) and git:

sudo apt-get install build-essential cmake git

Next you need to install opencv, please follow the guide in the Toolchain section.

Compile ROBOT

Change into your home directory and clone the git project:

git clone

Change into the directory and create a build folder:


mkdir build

cd build

Next run cmake:

cmake ..

Then compile the program:


Finally run the program:

./OrangeBall [params]

Command line parameters

To get all available parameters run:

./OrangeBall --help


ROBOT Orange Ball-shaped Object Tracker - An algorithm to detect the ball for RoboCup-Junior Soccer







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