[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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232 K. Daniec et al. [4] Jung, D., Tsiotras, P.: Modeling and Hardware-in-the-Loop Simulation for a Small Unmanned Aerial Vehicle, Georgia Institute of Technology, Atlanta, GA, 30332- 0150 [5] Sorton, E.F., Hammaker, S.: Simulated Flight Testing of an Autonomous Unmanned Aerial Vehicle Using Flight-Gear, Infotech@Aerospace, Arlington, VA, AIAA 2005-7083 (September 2005) [6] Johnson, E.N., Fontaine, S.: Use of Flight Simulation to Complement Flight Testing of Low-cost UAVs. In: AIAA Modeling and Simulation Technologies Conference and Exhibit, Montreal, Canada, AIAA 2001-4059 (August 2001) [7] Procerus Technologies: Kestrel Autopilot System, Autonomous Autopilot and Ground Control for Small Unmanned Aerial Vehicles, UAV Flight Guide, Version 1.8 (December 10, 2012), http://www.procerusuav.com/Downloads/Manuals/UAV_ Flight_Guide.pdf [8] Mystkowski, A.: Robust control of unmanned aerial vehicle - simulation investigations, Białystok Technical University, Prace Instytutu Lotnictwa, Warsaw, pp. s.82– s.102 (2011) ISSN 0509-6669 216 [9] Konwacji, C.: Control algorithm of micro aerial vehicle flight in streets’ canyons based on vision system 4(3), s.76–s.86 (2010) [10] He, Z., VenkataramanIyer, R., Chandler, P.R.: Vision based UAV flight control and obstacle avoidance. In: American Control Conference (2006) [11] Rathinam, S., Kim, Z., Sengupta, R.: Vision-Based Monitoring of Locally Linear Structures Using an Unmanned Aerial Vehicle1. J. Infrastruct. Syst. Special Issue: New Sensors, Instrumentation, and Signal Interpretation 14, 52–63 (2008) [12] Świtoński, A., Bieda, R., Wojciechowski, K.: Multispectral imaging for supporting colonoscopy and gastroscopy diagnoses. In: Hippe, Z.S., Kulikowski, J.L., Mroczek, T. (eds.) Human - Computer Systems Interaction. AISC, vol. 99, pp. 131–145. Springer, Heidelberg (2012) [13] Ruszewski, A.: Stabilization of discrete-time systems by PID controllers, Zeszyty Naukowe. Silesian University of Technical, Institute of Automation, z. 145, pp. 171—176 (2006) [14] Drake, S.P.: Converting GPS Coordinates (ϕλh) to Navigation Coordinates (ENU), Surveillance Systems Division Electronics and Surveillance Research Laboratory (December 10, 2012), http://dspace.dsto.defence.gov.au/dspace/bitstream/ 1947/3538/1/DSTO-TN-0432.pdf [15] GMap.NET – Great Maps for Windows (December 10, 2012), http://greatmaps.codeplex.com/ [16] Iwaneczko, P., Jędrasiak, K., Daniec, K., Nawrat, A.: A prototype of unmanned aerial vehicle for image acquisition. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2012. LNCS, vol. 7594, pp. 87–94. Springer, Heidelberg (2012)
Feature Extraction and HMM-Based Classification of Gait Video Sequences for the Purpose of Human Identification Henryk Josiński, Daniel Kostrzewa, Agnieszka Michalczuk, Adam Świtoński, and Konrad Wojciechowski Abstract. The authors present results of the research on human recognition based on the video gait sequences from the CASIA Gait Database. Both linear (principal component analysis; PCA) and non-linear (isometric features mapping; Isomap and locally linear embedding; LLE) methods were applied in order to reduce data dimensionality, whereas a concept of hidden Markov model (HMM) was used for the purpose of data classification. The results of the conducted experiments formed the main subject of analysis of classification accuracy expressed by means of the Correct Classification Rate (CCR). Keywords: dimensionality reduction, gait-based human identification, Hidden Markov model, manifold learning. 1 Introduction Gait is defined as coordinated, cyclic combination of movements which results in human locomotion [1]. Gait is considered as one of behavioral biometric features. A unique advantage of gait as a biometric is that it offers potential for recognition at a distance or at low resolution or when other biometrics might not be perceivable [2] especially from UAVs [26]. Gait can be captured by twodimensional video cameras of surveillance systems or by much accurate motion capture (mocap) systems which acquire motion data as a time sequence of poses. Henryk Josiński ⋅ Daniel Kostrzewa ⋅ Agnieszka Michalczuk ⋅ Adam Świtoński ⋅ Konrad Wojciechowski Silesian University of Technology, Institute of Computer Science, Akademicka 16, 44-101 Gliwice, Poland e-mail: {henryk.josinski,adam.switonski,agnieszka.michalczuk, daniel.kostrzewa,konrad.wojciechowski}@polsl.pl A. Nawrat and Z. Kuś (Eds.): Vision Based Systems for UAV Applications, SCI 481, pp. 233–245. DOI: 10.1007/978-3-319-00369-6_15 © Springer International Publishing Switzerland 2013
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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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2 Design of Object Detection, Recog
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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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