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.
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CITATION STYLE
Massouh, N., Brigato, L., & Iocchi, L. (2019). RoboCup@Home-Objects: Benchmarking Object Recognition for Home Robots. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11531 LNAI, pp. 397–407). Springer. https://doi.org/10.1007/978-3-030-35699-6_31
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