How Far Can You Get By Combining Change Detection Algorithms?
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In this paper we investigate how state-of-the-art change detection algorithms can be combined and used to create a more robust change algorithm leveraging their individual peculiarities. We exploited Genetic Programming (GP) to automatically select the best algorithms, combine them in different ways, and perform the most suitable post-processing operations on the outputs of the algorithms. In particular, algorithms' combination and post-processing operations are achieved with unary, binary and n-ary functions embedded into the GP framework. Using different experimental settings for combining existing algorithms we obtained different GP solutions that we termed IUTIS (In Unity There Is Strength). These solutions are then compared against state-of-the-art change detection algorithms on the video sequences and ground truth annotations of the ChandeDetection.net (CDNET 2014) challenge. Results demonstrate that using GP, our solutions are able to outperform all the considered single state-of-the-art change detection algorithms, as well as other combination strategies.
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