Pith. sign in

REVIEW 1 cited by

MARIO: Modular and Extensible Architecture for Computing Visual Statistics in RoboCup SPL

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2209.09987 v1 pith:K2WLRK7L submitted 2022-09-20 cs.CV

Domenico D. Bloisi , Andrea Pennisi , Cristian Zampino , Flavio Biancospino , Francesco Laus , Gianluca Di Stefano , Michele Brienza , Rocchina Romano This is my paper
classification cs.CV
keywords mariorobocuparchitecturechallengecomputingextensiblemodularopen
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

This technical report describes a modular and extensible architecture for computing visual statistics in RoboCup SPL (MARIO), presented during the SPL Open Research Challenge at RoboCup 2022, held in Bangkok (Thailand). MARIO is an open-source, ready-to-use software application whose final goal is to contribute to the growth of the RoboCup SPL community. MARIO comes with a GUI that integrates multiple machine learning and computer vision based functions, including automatic camera calibration, background subtraction, homography computation, player + ball tracking and localization, NAO robot pose estimation and fall detection. MARIO has been ranked no. 1 in the Open Research Challenge.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. An LLM-Based Automatic Sportscast Solution for Robot Soccer Matches

    cs.CV 2026-07 conditional novelty 5.0 of 10

    A neuro-symbolic pipeline turns RoboCup robot-soccer video into real-time statistics and LLM-generated commentary, demonstrated on three German Open 2026 clips.

Pith tools