MARIO: Modular and Extensible Architecture for Computing Visual Statistics in RoboCup SPL
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 29 references that are in the map. First posted on arXiv 20 Sep 2022.
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10 of 29 references.
References · 10
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
- 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
- 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,209
- 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,162
- 2017Simple Online and Realtime Tracking with a Deep Association Metriccited by 910
- 2016Simple Online and Realtime Trackingcited by 868
- 2022StrongSORT: Make DeepSORT Great Againcited by 213
- 2021A Survey of Modern Deep Learning based Object Detection Modelscited by 98
- 2018Deep Cosine Metric Learning for Person Re-Identificationcited by 73
- 2018Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPosecited by 42
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Citing papers per year: 2026: 1.
1 paper.