This paper describes the aerial object recognition algorithm for on-board and stationary vision system. Suggested algorithm is intended to recognize the objects of a specific kind using the set of the reference objects defined by 3D models. The proposed algorithm based on the outer contour descriptor building. The algorithm consists of two stages: learning and recognition. Learning stage is devoted to the exploring of reference objects. Using 3D models we can build the database containing training images by rendering the 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the recognition stage of the algorithm. The recognition stage is focusing on estimating the similarity of the captured object and the reference objects by matching an observed image descriptor and the reference object descriptors. The experimental research was performed using a set of the models of the aircraft of the different types (airplanes, helicopters, UAVs). The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.
Pavel Babayan, Sergey Smirnov, and Valery Strotov, "The implementation of aerial object recognition algorithm based on contour descriptor in FPGA-based on-board vision system ," Proc. SPIE 10430, High-Performance Computing in Geoscience and Remote Sensing VII, 1043006 (Presented at SPIE Remote Sensing: September 12, 2017; Published: 5 October 2017); https://doi.org/10.1117/12.2277920.
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