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Real‐time surveillance detection system for medium‐altitude long‐endurance unmanned aerial vehicles
Author(s) -
Amanatiadis Angelos,
Bampis Loukas,
Karakasis Evangelos G.,
Gasteratos Antonios,
Sirakoulis Georgios
Publication year - 2017
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4145
Subject(s) - computer science , real time computing , pedestrian detection , terrain , speedup , acceleration , footprint , artificial intelligence , remote sensing , pedestrian , engineering , geography , physics , cartography , archaeology , classical mechanics , transport engineering , operating system
Summary The detection of ambiguous objects, although challenging, is of great importance for any surveillance system and especially for an unmanned aerial vehicle, where the measurements are affected by the great observing distance. Wildfire outbursts and illegal migration are only some of the examples that such a system should distinguish and report to the appropriate authorities. More specifically, Southern European countries commonly suffer from those problems due to the mountainous terrain and thick forests that contain. Unmanned aerial vehicles like the “Hellenic Civil Unmanned Air Vehicle” project have been designed to address high‐altitude detection tasks and patrol the borders and woodlands for any ambiguous activity. In this paper, a moment‐based blob detection approach is proposed that uses the thermal footprint obtained from single infrared images and distinguishes human‐ or fire‐sized and shaped figures. Our method is specifically designed so as to be appropriately integrated into hardware acceleration devices, such as General Purpose Computation on Graphics Processing Units (GPGPUs) and field programmable gate arrays, and takes full advantage of their respective parallelization capabilities succeeding real‐time performances and energy efficiency. The timing evaluation of the proposed hardware accelerated algorithm's adaptations shows an achieved speedup of up to 7 times, as compared to a highly optimized CPU‐only based version.