11 May 2015 Bio-inspired approach for intelligent unattended ground sensors
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Improving the surveillance capacity over wide zones requires a set of smart battery-powered Unattended Ground Sensors capable of issuing an alarm to a decision-making center. Only high-level information has to be sent when a relevant suspicious situation occurs. In this paper we propose an innovative bio-inspired approach that mimics the human bi-modal vision mechanism and the parallel processing ability of the human brain. The designed prototype exploits two levels of analysis: a low-level panoramic motion analysis, the peripheral vision, and a high-level event-focused analysis, the foveal vision. By tracking moving objects and fusing multiple criteria (size, speed, trajectory, etc.), the peripheral vision module acts as a fast relevant event detector. The foveal vision module focuses on the detected events to extract more detailed features (texture, color, shape, etc.) in order to improve the recognition efficiency. The implemented recognition core is able to acquire human knowledge and to classify in real-time a huge amount of heterogeneous data thanks to its natively parallel hardware structure. This UGS prototype validates our system approach under laboratory tests. The peripheral analysis module demonstrates a low false alarm rate whereas the foveal vision correctly focuses on the detected events. A parallel FPGA implementation of the recognition core succeeds in fulfilling the embedded application requirements. These results are paving the way of future reconfigurable virtual field agents. By locally processing the data and sending only high-level information, their energy requirements and electromagnetic signature are optimized. Moreover, the embedded Artificial Intelligence core enables these bio-inspired systems to recognize and learn new significant events. By duplicating human expertise in potentially hazardous places, our miniature visual event detector will allow early warning and contribute to better human decision making.
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Nicolas Hueber, Nicolas Hueber, Pierre Raymond, Pierre Raymond, Christophe Hennequin, Christophe Hennequin, Alexander Pichler, Alexander Pichler, Maxime Perrot, Maxime Perrot, Philippe Voisin, Philippe Voisin, Jean-Pierre Moeglin, Jean-Pierre Moeglin, "Bio-inspired approach for intelligent unattended ground sensors", Proc. SPIE 9494, Next-Generation Robotics II; and Machine Intelligence and Bio-inspired Computation: Theory and Applications IX, 94940S (11 May 2015); doi: 10.1117/12.2177400; https://doi.org/10.1117/12.2177400

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