Unmanned aerial vehicles (UAVs) are an active research field since several years. They can be applied in a large variety of different scenarios, and supply a test bed to investigate several unsolved problems such as path planning, control and navigation. Furthermore, with the availability of low cost, robust and small video cameras, UAV video has been one of the fastest growing data sources in the last couple of years. In other words, object detection and tracking as well as visual navigation has recently received a lot of attention. This paper proposes an advanced technology framework that, through the use of UAVs, allows to supervise a specific sensible area (i.e. traffic monitoring, dangerous zone and so on). In particular, one of the most cited real-rime visual tracker proposed in the literature, Struck, is applied on video sequences tipically supplied by UAVs equipped with amonocular camera. Furthermore in this paper is investigated on the feasibility to graft different features characterization into the original tracking architecture (replacing the orginal ones). The used feature extraction methods are based on Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG). Objects to be tracked could be selected manually or by means of advanced detection technique based, for example, on change detection or template matching strategies. The experimental results on well known benchmark sequences show as these features replacing improve the overall performances of the original considered real-time visual tracker.
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