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Copyright (c) 2013-, Filippo Basso and Matteo Munaro
{filippo.basso, matteo.munaro}@dei.unipd.it
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This package contains a library implementing the Online Adaboost algorithm and a demo file showing the learning process of an Online Adaboost classifier based on color features. An example image with 9 colored squares is used. A color classifier is learned for a selected square (by default it is the central one). As features, color features extracted from the RGB histogram of the target are used. The target square is used as positive example, while negative examples are randomly selected from the rest of the image. After every iteration, the confidence values of every square with respect to the classifier learned for the target square are shown. Moreover, the mean color value of the most weighted features is shown in a histogram where the height is proportional to the feature weight. The written number, instead, represents the volume of the corresponding parallelepiped.
For Online Adaboost, we used our implementation of the algorithm described in:
[1] H. Grabner and H. Bischof. On-line boosting and vision. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pages 260–267, Washington, DC, USA, 2006.
For the color features, we used the original implementation described in:
[2] F. Basso, M. Munaro, S. Michieletto and E. Menegatti. Fast and robust multi-people tracking from RGB-D data for a mobile robot. In Proceedings of the 12th Intelligent Autonomous Systems (IAS) Conference, Jeju Island (Korea), 2012.
If you use part of this code, please cite [2].
This package needs the open source computer vision library OpenCV. Once you installed OpenCV, you should go to the package directory and type:
cmake .
make
Tested with OpenCV 2.3.X and 2.4.X on Ubuntu 12.04 LTS.
After building the code, you have to go to the bin folder and type:
./demo_adaboost