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- W2115280327 abstract "This paper addresses the problem of real-time moving-object detection, classification and tracking in populated and dynamic environments. In this scenario, a mobile robot uses 2D laser range data to recognize, track and avoid moving targets. Most previous approaches either rely on pre-defined data features or off-line training of a classifier for specific data sets, thus eliminating the possibility to detect and track different-shaped moving objects. We propose a novel and adaptive technique where potential moving objects are classified and learned in real-time using a fuzzy ART neural network algorithm. Experimental results indicate that our method can effectively distinguish and track moving targets in cluttered indoor environments, while at the same time learning their shape." @default.
- W2115280327 created "2016-06-24" @default.
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- W2115280327 date "2009-10-01" @default.
- W2115280327 modified "2023-10-16" @default.
- W2115280327 title "Learning moving objects in a multi-target tracking scenario for mobile robots that use laser range measurements" @default.
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- W2115280327 doi "https://doi.org/10.1109/iros.2009.5353913" @default.
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