[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

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.