Abstract
Remote video surveillance of vast outdoor systems for structural health monitoring using e.g. drones is gaining rapid popularity. Many such systems are designed as truss structures, due to well-known mechanical reasons. A truss structure has interstices inherently porous, and hence no closed region or contour really represents useful properties or features of just foreground or just background. In this paper, we present a novel approach to segment and detect porous objects of truss-like structures in videos. Our approach is primarily based on modeling of such objects as composite shapes, organized in a structure called geometric lattices. We define a novel feature called shape density to classify and segment the truss region. The segmented region is then analyzed for various surveillance goals such as bending. The algorithm was tested against video data captured for many transmission towers along two different power grid corridors. We believe that our algorithm will be very useful for analysis of truss-like structures in many a outdoor vision applications.
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Sharma, H., Sebastian, T., Purushothaman, B. (2017). A Lattice-Theoretic Approach for Segmentation of Truss-Like Porous Objects in Outdoor Aerial Scenes. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_65
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DOI: https://doi.org/10.1007/978-3-319-59876-5_65
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