Abstract
Detection of moving objects in sequences of images is an important research field, with applications for surveillance, tracking and object recognition among others. An algorithm to estimate motion in video image sequences, with moving object distinction and differentiation, is proposed. The motion estimation is based in three consecutive RGB image frames, which are converted to gray scale and filtered, before being used to calculate optical flow, applying Gunnar Farnebäck’s method. The areas of higher optical flow are maintained and the areas of lower optical flow are discarded using Otsu’s adaptive threshold method. To distinguish between different moving objects, a border following method was applied to calculate each object’s contour. The method was successful detecting and distinguishing moving objects in different types of image datasets, including datasets obtained from moving cameras. This extended version contemplates more results obtained, using the demonstrated methodology, with other datasets.
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Notes
- 1.
INRIA CAVIAR database: http://homepages.inf.ed.ac.uk/rbf/CAVIARDATA1/ (last checked 2020-09-27).
- 2.
Green’s theorem brief explanation: https://en.wikipedia.org/wiki/Green’s theorem (last checked 16.08.2018).
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Mendes, P.A.S., Paulo Coimbra, A. (2021). Movement Detection and Moving Object Distinction Based on Optical Flow for a Surveillance System. In: Ao, SI., Gelman, L., Kim, H.K. (eds) Transactions on Engineering Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-15-8273-8_12
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