[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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152 A. Babiarz, R. Bieda, and K. Jaskot • Create two vectors, and , with common origin in mass center , . Initial length of these vectors should be 1. They should be at angle and to the main axis of inertia, respectively. Angles are directed and measured clockwise. • If the vector points to background pixel and vector points to pixel belonging to the object, then . • Conversely, if the vector points to background pixel and vector points to pixel belonging to the object, then . • If both vectors point to pixels belonging to the object, the length of these vectors is increased by one and the whole procedure is repeated. The algorithm is simple and efficient, however it requires that the shape created by background subtraction algorithm should not differ significantly from the reference design presented on Figure 6. 3 Obtained Results The vision application works under Linux. It is meant to be a centralized vision system providing vision information for all who will ask for it. That goal was achieved by implementing the application as a TCP/IP server, to which clients can connect and ask for vision information (Figure 8 and 9). The application was tested on a PC computer working under Ubuntu Linux. The computer is equipped with Pentium 4 3.00 GHz Hyper Threading processor, 512 MB DDR RAM and GeForce 5200 FX graphics card. Input images were acquired with Samsung SCC–331 CCD camera equipped with Ernitec lens ( = 6 - 12 mm, = 35.5 mm, 1:1.4). Images were then digitized by simple BT878-based frame-grabber card. The lighting was provided by four 500W halogen lamps installed 2 m over the corners of the playing field. The application was compiled by the new GNU C++ compiler in version 4 with use of the following optimization flags: -O3 -funroll-loops. On the test hardware the application uses approximately 50% of CPU time and about 20 MB of RAM. The framerate is constant and is equal to 30 frames per second, which is the limit imposed by the frame-grabber hardware. With better frame-grabber it should be possible to reach about 60 frames per second without any code re-factorization or optimization. Results of precise measurements of time needed for completing particular tasks by the vision algorithm are presented in Table 1. The figures were obtained by averaging 100 measurements. The vision server also responds very quickly, usual response is under 1 ms. High frame-rate and quick response of the server is crucial for the stability of all control algorithms that use vision information on the feedback loop, so there is always a place for further research here, however the delays introduced by the current version of vision application seem to be acceptable for broad range of control algorithms.
Vision System for Group of Mobile Robots 153 Fig. 8. Proper binarization threshold Table 1. Processing times of every step of vision algorithm No. Operation Times [ms] 1. Correction of Radial Distortion 8.4 2. Shadow Removal 0.98 3. Image Difference 4.0 4. Thresholding 0.42 5. Dilation 0.54 6. Flood Fill 0.20 7. Region Sorting and Median Filtering 0.45 8. Histogram Calculation 0.12 9. Classification 0.07 The vision algorithm proved to operate reliably. The adaptation of reference histograms works perfectly, which is clearly visible in the corners of the playing field where the lighting conditions are significantly different than near center of the playing field. The background subtraction algorithm is so robust that it works perfectly when the overall lighting conditions were changed by turning on additional lamp, even without adaptation enabled. Size and shape of color markers on top of each robots prevents from the situation where markers of robots being very close to each other are “joined” and constitute one, bigger marker, which of course leads to recognition errors. For this situation is very difficult to deal with, the algorithm assumes that it never happens and the responsibility for assuring that it really wouldn’t is in the design of markers.
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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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206 Practical Applications of Class
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Prototyping the Autonomous Flight A
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Feature Extraction and HMM-Based Cl
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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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