SIFT Vehicle Recognition with Semi-Synthetic Model Database

Abstract – Object recognition is an important problem that has many applications that are of interest to the United States Air Force (USAF). Recently the USAF released its update to Technology Horizons, a report that is designed to guide the science and technology direction of the Air Force. Technology Horizons specifically calls out for the need to use autonomous systems in essentially all aspects of Air Force operations [1]. Object recognition is a key enabler to autonomous exploitation of intelligence, surveillance, and reconnaissance (ISR) data which might make the automatic searching of millions of hours of video practical. In particular this paper focuses on vehicle recognition with Lowe’s Scale-invariant feature transform (SIFT) using a model database that was generated with semi-synthetic data. To create the model database we used a desktop laser scanner to create a high resolution 3D facet model. Then the 3D facet model was imported into LuxRender, a physics accurate ray tracing tool, and several views were rendered to create a model database. SIFT was selected because the algorithm is invariant to scale, noise, and illumination making it possible to create a model database of only a hundred original viewing locations which keeps the size of the model database reasonable.

After a few years of other work this is my re-entry back into pattern recognition.

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