This thesis deals with human body detection and gestures tracking in videosequences. First, processing of videosequences in general is described. Further, different methods of human body detection are described and represented by significant papers. The most of the attention is focused on detection by real AdaBoost algorithm based on Haar-like features and Edgelet features. The practical part starts with selection of method that is implemented in this thesis. This method is detection by real AdaBoost based on Haar-like features. Further, different options of videosequence processing in JAVA are researched with justification of choice OpenCV library with JavaCV wrapper, which is used in this thesis. In the end, application itself is described, including description of GUI and description of each class and its functionality.

The range of missions performed by Unmanned Aircraft Systems (UAS) has been steadily growing in the past decades thanks to continued development in several disciplines. The goal of increasing the autonomy of UAS's is widening the range of tasks which can be carried out without, or with minimal, external help. This thesis presents methods for increasing specific aspects of autonomy of UAS's operating both in outdoor and indoor environments where cameras are used as the primary sensors. First, a method for fusing color and thermal images for object detection, geolocation and tracking for UAS's operating primarily outdoors is presented. Specifically, a method for building saliency maps where human body locations are marked as points of interest is described. Such maps can be used in emergency situations to increase the situational awareness of first responders or a robotic system itself. Additionally, the same method is applied to the problem of vehicle tracking. A generated stream of geographical locations of tracked vehicles increases situational awareness by allowing for qualitative reasoning about, for example, vehicles overtaking, entering or leaving crossings. Second, two approaches to the UAS indoor localization problem in the absence of GPS-based positioning are presented. Both use cameras as the main sensors and enable autonomous indoor ight and navigation. The first approach takes advantage of cooperation with a ground robot to provide a UAS with its localization information. The second approach uses marker-based visual pose estimation where all computations are done onboard a small-scale aircraft which additionally increases its autonomy by not relying on external computational power.