# DeepSportradar-v1: Computer Vision Dataset for Sports Understanding with High Quality Annotations

arXiv: https://arxiv.org/abs/2208.08190 · HTML: https://mlsanity.com/paper/2208.08190/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 49 references that are in the map.
- First posted on arXiv 17 Aug 2022.

## Builds on (most cited first, 30 of 32)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://mlsanity.com/paper/1908.03195/) (2019)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)](https://mlsanity.com/paper/1711.09349/) (2017)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [Bag of Tricks and A Strong Baseline for Deep Person Re-identification](https://mlsanity.com/paper/1903.07071/) (2019)
- [Panoptic-DeepLab](https://mlsanity.com/paper/1910.04751/) (2019)
- [Learning Discriminative Features with Multiple Granularities for Person Re-Identification](https://mlsanity.com/paper/1804.01438/) (2018)
- [Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro](https://mlsanity.com/paper/1701.07717/) (2017)
- [Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID](https://mlsanity.com/paper/2006.02713/) (2020)
- [FastReID: A Pytorch Toolbox for General Instance Re-identification](https://mlsanity.com/paper/2006.02631/) (2020)
- [SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos](https://mlsanity.com/paper/1804.04527/) (2018)
- [SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos](https://mlsanity.com/paper/2011.13367/) (2020)
- [Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer](https://mlsanity.com/paper/2106.04095/) (2021)
- [Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers](https://mlsanity.com/paper/2103.12340/) (2021)
- [Unsupervised Pre-training for Person Re-identification](https://mlsanity.com/paper/2012.03753/) (2020)
- [Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth](https://mlsanity.com/paper/1906.11109/) (2019)
- [SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos](https://mlsanity.com/paper/2204.06918/) (2022)
- [Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting](https://mlsanity.com/paper/2104.09333/) (2021)
- [Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch](https://mlsanity.com/paper/1910.10093/) (2019)
- [Sports Camera Calibration via Synthetic Data](https://mlsanity.com/paper/1810.10658/) (2018)
- [Robust Instance Segmentation through Reasoning about Multi-Object Occlusion](https://mlsanity.com/paper/2012.02107/) (2020)
- [Ball 3D Localization From A Single Calibrated Image](https://mlsanity.com/paper/2204.00003/) (2022)

## Built on by (most cited first, 30 of 37)

- [Body Part-Based Representation Learning for Occluded Person Re-Identification](https://mlsanity.com/paper/2211.03679/) (2022)
- [SoccerNet-Caption: Dense Video Captioning for Soccer Broadcasts Commentaries](https://mlsanity.com/paper/2304.04565/) (2023)
- [VARS: Video Assistant Referee System for Automated Soccer Decision Making from Multiple Views](https://mlsanity.com/paper/2304.04617/) (2023)
- [SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap](https://mlsanity.com/paper/2404.11335/) (2024)
- [X-VARS: Introducing Explainability in Football Refereeing with Multi-Modal Large Language Model](https://mlsanity.com/paper/2404.06332/) (2024)
- [SoccerNet 2023 Challenges Results](https://mlsanity.com/paper/2309.06006/) (2023)
- [Keypoint Promptable Re-Identification](https://mlsanity.com/paper/2407.18112/) (2024)
- [Towards Active Learning for Action Spotting in Association Football Videos](https://mlsanity.com/paper/2304.04220/) (2023)
- [A Universal Protocol to Benchmark Camera Calibration for Sports](https://mlsanity.com/paper/2404.09807/) (2024)
- [Multi-task Learning for Joint Re-identification, Team Affiliation, and Role Classification for Sports Visual Tracking](https://mlsanity.com/paper/2401.09942/) (2024)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [Towards AI-Powered Video Assistant Referee System (VARS) for Association Football](https://mlsanity.com/paper/2407.12483/) (2024)
- [CLIP-ReIdent: Contrastive Training for Player Re-Identification](https://mlsanity.com/paper/2303.11855/) (2023)
- [Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis](https://mlsanity.com/paper/2308.00323/) (2023)
- [Deep learning for action spotting in association football videos](https://mlsanity.com/paper/2410.01304/) (2024)
- [MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark](https://mlsanity.com/paper/2403.20225/) (2024)
- [VIPriors 3: Visual Inductive Priors for Data-Efficient Deep Learning Challenges](https://mlsanity.com/paper/2305.19688/) (2023)
- [TrackID3x3: A Dataset and Algorithm for Multi-Player Tracking with Identification and Pose Estimation in 3x3 Basketball Full-court Videos](https://mlsanity.com/paper/2503.18282/) (2025)
- [SoccerNet-v3D: Leveraging Sports Broadcast Replays for 3D Scene Understanding](https://mlsanity.com/paper/2504.10106/) (2025)
- [KaliCalib: A Framework for Basketball Court Registration](https://mlsanity.com/paper/2209.07795/) (2022)
- [Towards Learning Monocular 3D Object Localization From 2D Labels using the Physical Laws of Motion](https://mlsanity.com/paper/2310.17462/) (2023)
- [Enhanced Multi-Object Tracking Using Pose-based Virtual Markers in 3x3 Basketball](https://mlsanity.com/paper/2412.06258/) (2024)
- [Context-Aware 3D Object Localization from Single Calibrated Images: A Study of Basketballs](https://mlsanity.com/paper/2309.03640/) (2023)
- [M"obius Transform for Mitigating Perspective Distortions in Representation Learning](https://mlsanity.com/paper/2405.02296/) (2024)
- [MUDD: A New Re-Identification Dataset with Efficient Annotation for Off-Road Racers in Extreme Conditions](https://mlsanity.com/paper/2311.08488/) (2023)
- [Investigating Event-Based Cameras for Video Frame Interpolation in Sports](https://mlsanity.com/paper/2407.02370/) (2024)
- [Instance Segmentation under Occlusions via Location-aware Copy-Paste Data Augmentation](https://mlsanity.com/paper/2310.17949/) (2023)
- [Mutual Distillation Learning For Person Re-Identification](https://mlsanity.com/paper/2401.06430/) (2024)
- [Beyond the Mud: Datasets and Benchmarks for Computer Vision in Off-Road Racing](https://mlsanity.com/paper/2402.08025/) (2024)
- [VIPriors 4: Visual Inductive Priors for Data-Efficient Deep Learning Challenges](https://mlsanity.com/paper/2406.18176/) (2024)

## Abstract (arXiv)

With the recent development of Deep Learning applied to Computer Vision, sport video understanding has gained a lot of attention, providing much richer information for both sport consumers and leagues. This paper introduces DeepSportradar-v1, a suite of computer vision tasks, datasets and benchmarks for automated sport understanding. The main purpose of this framework is to close the gap between academic research and real world settings. To this end, the datasets provide high-resolution raw images, camera parameters and high quality annotations. DeepSportradar currently supports four challenging tasks related to basketball: ball 3D localization, camera calibration, player instance segmentation and player re-identification. For each of the four tasks, a detailed description of the dataset, objective, performance metrics, and the proposed baseline method are provided. To encourage further research on advanced methods for sport understanding, a competition is organized as part of the MMSports workshop from the ACM Multimedia 2022 conference, where participants have to develop state-of-the-art methods to solve the above tasks. The four datasets, development kits and baselines are publicly available.