---
title: 'TTA: Occlusion-Rich Table Tennis Dataset'
url: https://www.emergentmind.com/topics/table-tennis-australia-tta-dataset
type: topic
---

# TTA: Occlusion-Rich Table Tennis Dataset

Searching arXiv for the TTA dataset and closely related table-tennis datasets to ground the article in the cited literature.
Searching arXiv for "Table Tennis Australia TOTNet TTA occlusion-rich dataset".
Searching arXiv for related datasets: OpenTTGames, TTStroke-21, and P²ANet for context and comparison.
The Table Tennis Australia dataset, abbreviated **TTA**, is an **occlusion-rich table tennis tracking dataset** introduced as a benchmark for ball tracking under **partial and full occlusions** in realistic play. It was developed in collaboration with **Paralympics Australia**, collected from **professional-level Paralympic table tennis matches**, and positioned as a resource for evaluating **single-view** ball localization under challenging visibility conditions. In the paper that introduces it, TTA is defined by **9,159 samples** at **25 fps** and **\(1080 \times 1920\)** resolution, with **1,996 occlusion samples**, and serves as the central table-tennis benchmark for the occlusion-aware tracking model TOTNet [2508.09650].

## 1. Identity and provenance

TTA is explicitly presented as **“a new occlusion-rich table tennis dataset”** and as **“a manually annotated table tennis dataset”** collected from **four professional-level Paralympic table tennis matches**. The work was developed **“in collaboration with Paralympics Australia”**, and the labels were **reviewed with national team analysts to ensure quality** [2508.09650].

Its motivation is tightly coupled to a specific failure mode of racket-sport perception systems: existing datasets often contain **limited occlusion cases**, lower frame rates, or no explicit visibility labels. TTA was therefore designed to reflect **real-world gameplay conditions** in which the ball is frequently hidden by players, equipment, or viewpoint geometry. The paper presents this as a dataset for **object tracking performance under challenging occlusion scenarios**, rather than as a stroke-recognition, rally-outcome, or multimodal biomechanics corpus [2508.09650].

A recurrent source of confusion in the literature is that several table-tennis resources are sometimes informally grouped under generic abbreviations. In strict bibliographic terms, however, the **TTA** name is used in the TOTNet work; other table-tennis papers describe different datasets under different names, including **OpenTTGames**, **TTStroke-21**, and **P\(^2\)ANet** [2004.09927] [2112.11384] [2207.12730].

## 2. Acquisition protocol and dataset structure

The dataset is reported as comprising **9,159 samples** at **25 fps** and **\(1080 \times 1920\)** resolution, drawn from **four professional-level Paralympic table tennis matches**. The benchmark is explicitly described as **occlusion-rich**, with **1,996 occlusion samples**, attributed to **camera angles, ball size, and dynamic play** [2508.09650].

The annotation protocol follows **standard visibility labels**, with three categories used in the TTA benchmark:

- **Fully Visible**
- **Partially Occluded**
- **Fully Occluded**

The paper provides the following split-level visibility breakdown for TTA [2508.09650]:

| Split | Fully Visible | Partially Occluded | Fully Occluded |
|---|---:|---:|---:|
| Train | 5,141 | 834 | 650 |
| Validation | 1,285 | 215 | 158 |
| Test | 668 | 72 | 67 |

In the reported experiments, the tracking model uses a **configurable number of input frames**, with **five frames** selected as the best trade-off between temporal context and efficiency. A plausible implication is that TTA is intended not merely as a framewise ball-localization set, but as a benchmark for **short-horizon temporal inference** under occlusion [2508.09650].

The paper does not provide a match-by-match metadata inventory, nor does it enumerate player identities, venues, or camera calibration parameters. It does state, however, that the data come from **professional-level Paralympic matches** and were curated specifically to benchmark tracking in **real-world** conditions rather than in laboratory or minimally occluded settings [2508.09650].

## 3. Annotation scheme and benchmark formulation

TTA provides **2D ball coordinates** and **visibility labels** for each annotated target frame. The visibility taxonomy distinguishes **fully visible**, **partially occluded**, and **fully occluded** cases, enabling both stratified evaluation and visibility-aware training [2508.09650].

In the TOTNet benchmark formulation, ground truth is represented differently depending on visibility. For **fully visible** and **partially occluded** frames, the target is encoded as **one-hot heatmaps** over the horizontal and vertical image axes. For **fully occluded** frames, the target becomes a **normalized Gaussian distribution** centered at the labeled position, explicitly modeling positional uncertainty under total visual absence [2508.09650].

This label structure is used in a **visibility-weighted BCE loss**:
\[
L = w_v \cdot \left( \mathrm{BCE}(P_x, T_{x,\mathrm{map}}) + \mathrm{BCE}(P_y, T_{y,\mathrm{map}}) \right),
\]
where \(v\) indexes visibility level and \(w_v\) is the associated weight. The dataset therefore supports training regimes in which **occluded frames contribute differently from visible ones**, rather than being treated as ordinary localization errors [2508.09650].

Evaluation on TTA uses two principal metrics:

- **RMSE**, computed from Euclidean pixel error between predicted and labeled ball coordinates.
- **Accuracy**, defined by a distance threshold:
  - **5 pixels** for **fully visible** and **partially occluded** frames,
  - **10 pixels** for **fully occluded** frames.

This asymmetric thresholding is a consequential design choice. It encodes the fact that full occlusion is not simply a harder version of ordinary detection, but a distinct inference regime in which label uncertainty and temporal reasoning are central [2508.09650].

## 4. Empirical role in the TOTNet benchmark

TTA is the central table-tennis benchmark used to compare **TTNet**, **TrackNetV2**, **monoTrack**, **WASB**, and **TOTNet** under visibility-stratified evaluation. Its main contribution is not merely aggregate difficulty, but the fact that it reveals large performance separations specifically in the **fully occluded** regime [2508.09650].

On TTA, the strongest reported model is **TOTNet (OF)**, which combines temporal modeling with optical flow. Its reported performance is:

- **Visible:** RMSE **1.84**, Accuracy **0.98**
- **Partially Occluded:** RMSE **3.45**, Accuracy **0.92**
- **Fully Occluded:** RMSE **7.19**, Accuracy **0.80**

By contrast, the strong baseline **WASB** reports:

- **Visible:** RMSE **2.14**, Accuracy **0.97**
- **Partially Occluded:** RMSE **5.26**, Accuracy **0.88**
- **Fully Occluded:** RMSE **37.30**, Accuracy **0.63**

The fully occluded case is the decisive one: TTA exposes a large gap between methods that remain competent when the ball disappears entirely and methods that primarily interpolate visible detections [2508.09650].

The ablation study is also conducted on TTA. It shows that **visibility-weighted BCE** improves full-occlusion performance, **occlusion augmentation** becomes effective only when combined with such weighting, and adding **optical flow** further reduces fully occluded RMSE from **12.31** to **7.19** while increasing accuracy from **0.74** to **0.80** [2508.09650]. This indicates that TTA is not merely a passive evaluation set; it is the empirical substrate on which the paper’s modeling claims are established.

## 5. Relation to other table-tennis datasets

TTA occupies a distinct niche within the table-tennis dataset landscape. It is neither a stroke-taxonomy benchmark nor a multimodal biomechanics corpus; it is a **visibility-annotated ball-tracking benchmark** centered on **occlusion robustness** [2508.09650].

Several other datasets address different problems:

**OpenTTGames** provides **120 fps** **\(1920 \times 1080\)** videos with event labels, semantic segmentation masks, and ball coordinates for multi-task analysis, but the TTA paper characterizes the TTNet/OpenTTGames-style setting as having **minimal occlusion** and lacking explicit visibility labels [2004.09927] [2508.09650].

**TTStroke-21** is a video benchmark for fine-grained **stroke detection and classification**, with **20 stroke classes** in the MediaEval formulation and HD **120 fps** recordings under natural conditions. Its annotation target is stroke boundaries and stroke identity, not ball visibility under occlusion [2112.11384] [2301.13576].

**P\(^2\)ANet** is a broadcast-based dense action detection benchmark with **2,721 video clips**, **139,075** labeled stroke segments, and **14** fine-grained action classes. It addresses dense temporal action localization and recognition from **25 FPS** broadcasting videos rather than ball localization under visibility uncertainty [2207.12730].

**RacketVision** extends the problem space toward **ball tracking**, **racket pose estimation**, and **trajectory forecasting**, and its table-tennis subset contains **50 games**, **780 clips**, **170,027 frames**, **19,495** ball annotations, and **6,648** racket annotations. It broadens multimodal sports analytics, but its principal novelty is ball–racket interaction modeling rather than occlusion-stratified ball tracking [2511.17045].

A common misconception is therefore to treat “table tennis dataset” as a single category. TTA is much narrower and more specialized: it is a dataset for **occlusion-aware 2D ball tracking**, particularly valuable when the research question is whether a model can infer ball location when direct visual evidence is partial or absent [2508.09650].

## 6. Access, constraints, and research significance

The paper states that **“The dataset will be shared upon academic request”** and points to the project repository at **`https://github.com/AugustRushG/TOTNet`** for code and access information. No explicit public license is specified in the paper text [2508.09650].

This access model matters because TTA is built from **professional-level Paralympic matches** and incorporates annotations reviewed with **national team analysts**. The paper does not formalize a long-term benchmark server, leaderboard, or redistribution policy. As a result, TTA currently appears closer to a **controlled academic resource** than to an unrestricted public archive [2508.09650].

Its research significance lies in three properties. First, it provides a **visibility-aware annotation protocol** that cleanly separates **fully visible**, **partially occluded**, and **fully occluded** cases. Second, it supplies enough difficult samples—especially **1,996 occlusion samples**—to make fully occluded tracking a measurable problem rather than an anecdotal edge case. Third, it shifts table-tennis ball tracking away from pure detection and toward **temporal inference under uncertainty**, which is precisely the regime needed for downstream applications such as **post-match analytics**, **coaching**, and **referee support** [2508.09650].

In that sense, TTA is best understood not as a general-purpose table-tennis dataset, but as a specialized benchmark that operationalizes one of the field’s hardest perceptual conditions: recovering the ball state when the ball is small, fast, and frequently hidden.

Source: https://www.emergentmind.com/topics/table-tennis-australia-tta-dataset