This paper presents a benchmark for object recognition inspired by RoboCup@Home competition and thus focusing on home robots. The benchmark includes a large-scale training set of 196K images labelled with classes derived from RoboCup@Home rulebooks, two medium-scale test sets (one taken with a Pepper robot) with different objects and different backgrounds with respect to the training set, a robot behavior for image acquisition, and several analysis of the results that are useful both for RoboCup@Home Technical Committee to define competition tests and for RoboCup@Home teams to implement effective object recognition components.
RoboCup@ Home-Objects: benchmarking object recognition for home robots / Massouh, Nizar; Brigato, Lorenzo; Iocchi, Luca. - 11531:(2019), pp. 397-407. (Intervento presentato al convegno 23rd Annual RoboCup International Symposium, RoboCup 2019 tenutosi a Sydney; Australia) [10.1007/978-3-030-35699-6_31].
RoboCup@ Home-Objects: benchmarking object recognition for home robots
Nizar Massouh
Primo
;Lorenzo Brigato
;Luca Iocchi
2019
Abstract
This paper presents a benchmark for object recognition inspired by RoboCup@Home competition and thus focusing on home robots. The benchmark includes a large-scale training set of 196K images labelled with classes derived from RoboCup@Home rulebooks, two medium-scale test sets (one taken with a Pepper robot) with different objects and different backgrounds with respect to the training set, a robot behavior for image acquisition, and several analysis of the results that are useful both for RoboCup@Home Technical Committee to define competition tests and for RoboCup@Home teams to implement effective object recognition components.| File | Dimensione | Formato | |
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Massouh_Postprint_RoboCup_2019.pdf
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Note: https://link.springer.com/chapter/10.1007/978-3-030-35699-6_31
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