[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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88 Z. Kuś and A. Nawrat Fig. 13. Responses of signals and in case of excitations , and disturbances Fig. 14. Responses of signals and in a case of excitation , and disturbances for the system with a lead compensator PD
Object Tracking in a Picture during Rapid Camera Movements 89 30 · 1 1 · 0.01 (8) The compensator was tuned in such way that it is reducing dominant time constant [24]. For system with lead compensator PD the responses of signals and in a case of excitations , and , which are the same as the above-mentioned, are presented in figure 14. As figure 14 presents the control system works excellent in a steady state and in transients. The change of , and , causes the change of the required angle but current value of camera rotation is following signal very closely. There are not overshoots. The camera head turns continuously in order to set the camera rotation angle to the set value . This way the camera is all time directed towards the tracked object. 5 Summary The paper presents camera head control algorithm for Unmanned Aerial Vehicles. What is more, the presented solution is complementary for methods based on image processing. Interestingly, the most important feature of the presented solution is the use of information from IMU (Inertial Measurement Unit) [27] and GPS (Global Positioning System). It is significant enrichment of the range of tools and data which are used. The tracking of the objects, which are located on the ground, was considered in the paper. It was assumed that image processing algorithms are used and produce information about location of the object in the picture. UAV behavior depends on the gusts and rapid changes of the wind. The tracked object can be lost from the camera field of view in the above-mentioned case. Thus it was necessary to seek other sources of data - sources different then image processing system. What was also discussed herein is the case when the object which is located on flat ground is tracked by the helicopter with the camera. The presented solution was based on calculation of the object position in the moment when the object is in the center of the picture. The current camera position, orientation and the calculated object position were indispensable information for the control algorithm. The examples which were presented herein demonstrate a correct behavior of the camera head control system. The similar thought process could be applied to various different mobile robots [29] and implemented in their navigation systems [31]. References [1] Davies, Palmer, P.L., Mirmehdi, M.: Detection and tracking of very small low contrast objects. In: Proceedings of the 9th British Machine Vision Conference (September 1998) [2] Zhang, S., Karim, M.A.: Automatic target tracking for video annotation. Op. Eng. 43, 1867–1873 (2004)
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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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8 A. Babiarz et al. a) b) Fig. 5. T
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10 A. Babiarz et al. o o o “micvo
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12 A. Babiarz et al. a) b) Fig. 7.
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14 A. Babiarz et al. • The camera
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16 A. Babiarz et al. the camera ser
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18 A. Babiarz et al. a) b) Fig. 11.
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20 A. Babiarz et al. patrol point i
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22 A. Babiarz et al. particular ang
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24 A. Babiarz et al. The sample cod
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Recognition and Location of Objects
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Vision System for Group of Mobile R
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A Distributed Control Group of Mobi
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178 D. Bereska et al. The aim of th
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180 D. Bereska et al. 3 Control Alg
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182 D. Bereska et al. Fig. 5. Schem
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184 D. Bereska et al. 4.1 Mechanica
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186 D. Bereska et al. 4.1.3 Open-Lo
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188 D. Bereska et al. Fig. 16. Comp
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Probabilistic Approach to Planning
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206 Practical Applications of Class
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208 M. Mellado and K. Skrzypczyk an
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210 M. Mellado and K. Skrzypczyk a
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216 M. Mellado and K. Skrzypczyk 6
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Prototyping the Autonomous Flight A
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Feature Extraction and HMM-Based Cl
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248 K. Daniec et al. In this articl
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250 K. Daniec et al. Fig. 2. Applic
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252 K. Daniec et al. by the ability
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254 K. Daniec et al. The idea of us
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Selection of Individual Gait Featur
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274 A. Nawrat et al. alia weather c
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280 A. Nawrat et al. • 1-phase el
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282 A. Nawrat et al. The phase cond
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284 A. Nawrat et al. Table 1. Compa
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286 A. Nawrat et al. P3 P3 P3 71101
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288 A. Nawrat et al. S, VA 4,510 4,
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290 A. Nawrat et al. Comparison dif
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Technology Development of Military
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312 A. Czornik, A. Nawrat, and M. N
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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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