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In recent years we have assisted to a growing interest for embedded vision, due to the availability of low cost hardware systems, effective for energy consumption, flexible for their size at the cost of limited (compared to the server) computing resources. Their use is boosted by the simplicity of their positioning in places where energy or network bandwidth is limited. Smart cameras are digital cameras embedding computer systems able to host video applications; due to the cost and the performance, they are progressively gaining popularity and conquering large amount of the market. Smart cameras are now able to host on board video applications, even if this imposes an heavy reformulation of the algorithms and of the software design so as to make them compliant with the limited CPUs and the small RAM and flash memory (typically of a few megabytes). In this paper we propose a method for gender recognition on video sequences, specifically designed for making it suited to smart cameras; although the algorithm uses very limited resources (in terms of RAM and CPU), it is able to run on smart cameras available today, presenting at the same time an high accuracy on unrestricted videos taken in real environments (malls, shops, etc.). [ABSTRACT FROM AUTHOR]

For several years, fault diagnosis of photovoltaic (PV) plants has been manually performed by the human operator by a visual inspection or automatically, by evaluating electrical measures collected by sensors mounted on each PV module. In recent years, a notable interest of the scientific community has been devoted towards the definition of algorithms able to automatically analyse the sequence of images acquired by a thermal camera mounted on board of an unmanned aerial vehicle (UAV) for early PV anomaly detection. In this paper, we define a model-based approach for the detection of the panels, which uses the structural regularity of the PV string and a novel technique for local hot spot detection, based on the use of a fast and effective algorithm for finding local maxima in the PV panel region. Finally, we introduce the concept of global hot spot detection, namely a multi-frame recognition of PV faults which further improves the anomaly detection accuracy of the proposed method. The algorithm has been designed and optimized so as to run in real-time directly on an embedded system on board of the UAV. The accuracy of the proposed approach has been experimented on several video sequences with a standard protocol in terms of Precision, Recall and F-Score, so that our dataset and our quantitative results can be used for future comparisons and to evaluate the reliability of computer vision techniques designed for thermographic PV inspection. [ABSTRACT FROM AUTHOR]