[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

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146 A. Babiarz, R. Bieda, and K. Jaskot 2.3 Input Image Format The vision algorithm operates in RGB color space. It has the advantage of being directly available from the frame-grabber hardware, so there is no need for color space conversion. Additionally, working in the RGB color space isintuitive and the visualization and analysis of RGB images and histograms is very easy for human eye. The RGB color space however is not ideal and suffers from nonlinear shift of colors happening after change to the intensity of light. Fortunately, effects of this undesired shift can be easily removed by the adaptation of the algorithm which is described below. In theory, there are other color spaces like HSV or HSL, in which color information is H and S channels should not be vulnerable to changes in the intensity of light. However, experiments proved that although they really are better than RGB color space in preserving the color information unchanged despite of the change of lighting conditions, HSV or HSL histograms also require adaptation, like for RGB. So there is really no significant advantage of using these color spaces. 2.4 Adaptation of Background Model Although the background subtraction algorithm isn’t very sensitive to moderate changes of ambient lighting, it is desirable to give it the ability to update the background model in order to ensure its proper operation during long periods of time. The problem here is not in choosing how to update the background (the moving average algorithm with learning factor about 0.1 is good enough) but rather in deciding which pixels should be updated. In theory all pixels classified as belonging to the background should be the subject to update, practice however shows that this method leads to pollution of background model with pixels coming from fuzzy edges and from shadow of objects. The solution is to update only pixels lying outside of object’s bounding boxes, enlarged twice in order to ensure that no pixels from object’s shadow or fuzzy edge will contribute to the update of background model. So the whole procedure of the adaptation of background model can be expressed in the formula: , , , 1 , , , (8) , , where is a set of pairs , being coordinates of pixels in the image, is an image being actual background model, is input image, is set of pairs , being coordinates of all pixels inside two times enlarged bounding boxes, found by flood fill algorithm. Enlargement is done in a way that center of bounding box remains in the same position. Indexes and 1 denote previous and current frame, respectively.
Vision System for Group of Mobile Robots 147 2.5 Adaptation of Reference Histograms The need for continuous adaptation of reference histograms defining the classes of objects comes from the unavoidable nonlinear shift of colors of pixels belonging to every object, which occurs as the object is moving around the playing field. The object has different color in different points on playing field mainly because of the non-uniform lighting provided by four lamps installed over the playing field. This phenomenon is in turn a cause of unpredictable changes in the object’s histogram which can lead to recognition errors. Fortunately this undesired effect can be compensated by simple moving average algorithm which, after successful recognition, updates the reference histogram with the histogram of an object recognized as belonging to the class defined by this reference histogram. The formula for histogram update is as follows: 1 (9) where is a set of all possible pixel values , is a normalized reference histogram being updated and is a normalized input histogram. And as above, indexes and 1 denote previous and current frame, respectively. For the objects on playing field are able to move really fast (up to 1 ), the learning factor is quite large here and is equal to 0.2. It is important to notice that histogram obtained by moving average algorithm from two normalized histograms remains normalized as well. The proof is easy and is presented below. First, the formula for calculating sum of bins in a histogram created from two normalized histograms according to the moving average algorithm: 1 Restructuring right-hand side yields 1 Factors and 1 can be taken outside summing operator yielding (10) (11) 1 Since right-hand side histograms are normalized (sum of bins in each histogram is equal to 1) (12) 1 (13) 1 (14)
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Studies in Computational Intelligen
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Aleksander Nawrat · Zygmunt Kuś E
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“In God We Trust, All others we o
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VIII Preface valuable information i
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Contents Part I: Design of Object D
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Contents XIII Technology Developmen
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XVI List of Contributors Adam Czorn
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XVIII List of Contributors Henryk M
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XX List of Contributors Józef Wron
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2 Design of Object Detection, Recog
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4 A. Babiarz et al. 1 Introduction
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6 A. Babiarz et al. The description
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14 A. Babiarz et al. • The camera
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Recognition and Location of Objects
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Probabilistic Approach to Planning
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206 Practical Applications of Class
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Prototyping the Autonomous Flight A
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344 Conclusions Control algorithms
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[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

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