13 April 2018 Graph-based people segmentation using a genetically optimized combination of classifiers
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Many approaches for background subtraction and people detection have been developed so far. However, the best state-of-the-art methods do not yet give satisfactory results in real transportation environments. Indeed, these latter configurations imply several difficulties such as fast brightness changes, noise, shadows, scrolling background, etc., and a single approach cannot deal with all these. We propose an approach for people segmentation and tracking in videos that is suited for real-world conditions. Our strategy combines several state-of-the-art methods for people detection, silhouette appearance modeling and tracking. Each process also uses its own frame preprocessing pipeline. The optimal combination of the people classifiers used, as well as the optimal parameters of each of the combined methods, being too difficult to be determined altogether, a genetic algorithm is used to determine the optimal classifier parameters and their combination weights. The output of the latter is used as an initialization for a multiframe graph-cut operating on superpixel graphs. Our proposed approach is evaluated on the BOSS European project database that was acquired in moving trains and contains typical scientific locks encountered in real transportation systems.
© 2018 SPIE and IS&T 1017-9909/2018/$25.00 © 2018 SPIE and IS&T
Cyril Meurie, Cyril Meurie, Olivier Lézoray, Olivier Lézoray, Christophe Coniglio, Christophe Coniglio, Marion Berbineau, Marion Berbineau, "Graph-based people segmentation using a genetically optimized combination of classifiers," Journal of Electronic Imaging 27(5), 051210 (13 April 2018). https://doi.org/10.1117/1.JEI.27.5.051210
Received: 22 December 2017; Accepted: 13 March 2018; Published: 13 April 2018


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