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In this paper visualization techniques for modern closed circuit television (CCTV) smart city services are discussed with application to prevention of threats. Unconventional approaches to the intelligent visual data processing are proposed in order to support video surveillance operators, thus to make their work less exhaustive and more effective. Although registration of a huge amount of video data requires development of intelligent and automatic signal processing information extraction techniques, improvement of visualization methods for operators is also a very important task, because of the crucial role the human factor plays and should always play in the decision making, e.g. in the operator reactions to various crisis situations, which can never be fully eliminated by artificial intelligence. Four software based mechanisms connected with a standard or with a slightly extended hardware are proposed as options for the CCTV operators. They utilize rather known ideas but are implemented with new extensions to original algorithms, as well as with additional, innovative modifications and solutions (not presented in the literature). With them they become reliable and efficient tools for the CCTV systems. First, generation of cylindrical panoramas is suggested in order to make long-time video content analysis of a defined area easier and faster. Using panoramas it is possible to reduce the time that is required to watch the video by a factor of hundreds or even thousands and perform an efficient compression of the video stream for the long-time storage. Second, the controlled stereovision option is discussed for quicker and more precise extraction of relevant information from the observed scene. Third, the thermo-vision is analyzed for faultless detection of pedestrians at night. Finally, a novel high dynamic range (HDR) technique is proposed, dedicated to the CCTV systems, in contrast to other typical entertainment oriented HDR approaches, for clear visualization of important and meaningful image details, otherwise invisible. We validated usefulness of the proposed techniques with many experiments presented in this paper.

The ACO (Ant Colony Optimization) algorithm is a bio-inspired metaheuristic used to optimize problems or functions described by graphs, sequences of events, or queues of tasks. It is used, among a variety of other purposes, when routing Internet network packets, determining the shortest routes between designated points (traveling salesman's problem), for the time and cost optimization of production, or setting public transport stops. In the article, the ACO algorithm was used to autonomously construct the optimal route for an unmanned aerial vehicle (UAV). The algorithm establishes the spatial orientation of the UAV, indicating the direction of its transition for each intermediate waypoint. The results of the simulations show the trajectory of the UAV depending on the selected weighting factors, determining the priority of avoiding detected hazards or choosing the shortest path. The quality of each variant is evaluated numerically by the calculated fitness function, the value of which is the sum of the costs of the transition to each intermediate route point. The effect of the algorithm is a set of executable trajectory variants, of which the one with the best fitness value is selected. Streszczenie. Algorytm ACO (ang. Ant Colony Optimization) jest bio-inspirowaną metaheurystyką, wykorzystywaną do optymalizacji problemów lub funkcji opisywanych za pomocą grafów, sekwencji zdarzeń, czy też kolejki zadań. Znajduje on zastosowanie m.in. przy trasowaniu pakietów sieci internetowych, wyznaczaniu najkrótszych tras między wyznaczonymi punktami (problem komiwojażera), optymalizacji czasu i kosztu produkcji, czy też ustalaniu przystanków transportu publicznego. W artykule, algorytm ACO został wykorzystany do autonomicznego wyznaczenia optymalnej trasy dla bezpilotowego statku powietrznego (BSP). Algorytm ustala orientację przestrzenną BSP, determinującą kierunek jego przemieszczenia dla każdego pośredniego punktu docelowego. Wyniki przeprowadzonych symulacji przedstawiają trajektorię BSP w zależności od dobranych współczynników wagowych, określających priorytet ominięcia wykrytych zagrożeń lub wybrania najkrótszej drogi. Jakość każdego wariantu jest określana liczbowo poprzez ustaloną funkcję dopasowania, której wartość stanowi suma kosztów przejścia do każdego pośredniego punktu trasy. Efektem działania algorytmu jest zbiór wykonywalnych wariantów trajektorii, z których wybrany zostaje ten o najlepszej wartości dopasowania [Zastosowaniealgorytmu ACO do wyznaczania trasy BSP]