Ball Tracking 同属一个任务的两个源，一起审、一起传：乒乓（OTTG）与网球（TrackNet）都是——
「INPUT = 一段比赛视频 → TARGET = 同一段视频，每帧用红圈把球标出来」。两源产出统一四件套、同一条编号，落进
同一个 final_v1/New_Tasks/Task_14_ball_tracking/，来源写在各自 metadata 里区分。
| 项 | 乒乓 OTTG | 网球 TrackNet | 说明 |
|---|---|---|---|
| 源格式 | mp4 1920×1080@120fps | jpg 图序列 1280×720 | 格式不同,渲染器各一入口 |
| 1 sample = | 1 回合 | 1 clip(1 段来回) | 连续片段,≤128 帧 |
| fps / 长度 | 30fps · ~4s | 25fps · ~5s | 正常长度 |
| 画幅 / 编码 | 640×360 · H.264 · yuv420p | 浏览器可放,符合指南 | |
| 球标记 | 红色空心圈 r=14px,套在球坐标上;遮挡/未标注帧不画 | 坐标真值来自各自标注 | |
| 球标注密度 | ~56%(稀疏) | ~89%(密) | 网球标注更全,圈几乎不闪 |
固定单机位、球坐标来自 ball_markup.json。球被球拍/球员挡住的帧没标注 → 不画圈(圈会"闪",约一半帧有)。
{
"schema_version": "v2v-metadata/2.0",
"task_id": "Task_14_ball_tracking",
"sample_id": "000000000",
"pipeline_id": "Task_14_ball_tracking",
"created_at": null,
"generation_mode": "video_to_video",
"transformation": {
"kind": "ball_localization_overlay",
"overlay": "target = input video with the ball circled each frame (no time shift; frame-aligned)",
"marker": {
"shape": "circle",
"color_rgb": [
237,
28,
36
],
"radius_px": 14,
"stroke_px": 3,
"drawn_when": "ball annotated/visible",
"absent_when": "occluded or unlabeled -> no circle"
},
"rally": {
"native_frame_start": 1413,
"native_frame_end": 2460,
"frames_marked": 72,
"frames_total": 128,
"note": "one rally = ball-annotation frames grouped with gap < 150 native frames"
},
"ball_track": {
"note": "per output-frame ball pixel (x,y) ground truth from OTTG ball_markup.json; null = no circle drawn (occluded/unlabeled)",
"coord_space": "pixels in source 1920x1080 (marker rescaled to 640-wide output)",
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},
"video": {
"num_frames_input": 128,
"num_frames_target": 128,
"fps": 30,
"resolution": [
640,
360
],
"duration_s": 4.267,
"source_media": "video",
"frame_source": "video"
},
"prompt": {
"v2v_instruction": "This is a fixed-camera video of a table-tennis rally. Track the ball: produce a copy of the video in which the ball is marked in every frame with a red circle drawn at its current position. When the ball is occluded, off-screen, or otherwise not visible, draw no circle. Keep everything else in the frame identical to the input — same camera, scene, players and timing — and add only the ball marker."
},
"label_space": [
{
"phrase": "ball",
"marker": "red circle"
}
],
"domain": {
"field": "video_understanding",
"kind": "fixed_camera_real_video",
"attributes": {
"subtype": [
"ball_tracking",
"object_localization",
"table_tennis"
],
"sport": "table_tennis"
},
"source": {
"dataset": "OpenTTGames",
"clip": "test_1.mp4"
}
},
"source_dataset": {
"dataset": "OpenTTGames (OSAI TTNet)",
"license": "CC BY-NC-SA 4.0",
"origin": "Voeikov et al., 'TTNet: Real-time temporal and spatial video analysis of table tennis', CVPRW 2020",
"url": "https://lab.osai.ai/"
},
"integrity": {
"input_sha256": "81bfb1528a2569d5cf97f48b534614d981e6fc2de69c4f45c1f3f980a92e9eb8",
"target_sha256": "796fe39ec46ab341fb7872ea583ee62f1f1704ad7ec5e366477ba88dd7c66453"
},
"provenance": {
"engine": "v2v_taxonomy",
"engine_version": "1.0.0",
"pipeline_version": "v0.1.0-sample"
}
}This is a fixed-camera video of a table-tennis rally. Track the ball: produce a copy of the video in which the ball is marked in every frame with a red circle drawn at its current position. When the ball is occluded, off-screen, or otherwise not visible, draw no circle. Keep everything else in the frame identical to the input — same camera, scene, players and timing — and add only the ball marker.
{
"schema_version": "v2v-metadata/2.0",
"task_id": "Task_14_ball_tracking",
"sample_id": "000000001",
"pipeline_id": "Task_14_ball_tracking",
"created_at": null,
"generation_mode": "video_to_video",
"transformation": {
"kind": "ball_localization_overlay",
"overlay": "target = input video with the ball circled each frame (no time shift; frame-aligned)",
"marker": {
"shape": "circle",
"color_rgb": [
237,
28,
36
],
"radius_px": 14,
"stroke_px": 3,
"drawn_when": "ball annotated/visible",
"absent_when": "occluded or unlabeled -> no circle"
},
"rally": {
"native_frame_start": 186,
"native_frame_end": 693,
"frames_marked": 72,
"frames_total": 127,
"note": "one rally = ball-annotation frames grouped with gap < 150 native frames"
},
"ball_track": {
"note": "per output-frame ball pixel (x,y) ground truth from OTTG ball_markup.json; null = no circle drawn (occluded/unlabeled)",
"coord_space": "pixels in source 1920x1080 (marker rescaled to 640-wide output)",
"per_frame": [
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"video": {
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"num_frames_target": 127,
"fps": 30,
"resolution": [
640,
360
],
"duration_s": 4.233,
"source_media": "video",
"frame_source": "video"
},
"prompt": {
"v2v_instruction": "This is a fixed-camera video of a table-tennis rally. Track the ball: produce a copy of the video in which the ball is marked in every frame with a red circle drawn at its current position. When the ball is occluded, off-screen, or otherwise not visible, draw no circle. Keep everything else in the frame identical to the input — same camera, scene, players and timing — and add only the ball marker."
},
"label_space": [
{
"phrase": "ball",
"marker": "red circle"
}
],
"domain": {
"field": "video_understanding",
"kind": "fixed_camera_real_video",
"attributes": {
"subtype": [
"ball_tracking",
"object_localization",
"table_tennis"
],
"sport": "table_tennis"
},
"source": {
"dataset": "OpenTTGames",
"clip": "test_1.mp4"
}
},
"source_dataset": {
"dataset": "OpenTTGames (OSAI TTNet)",
"license": "CC BY-NC-SA 4.0",
"origin": "Voeikov et al., 'TTNet: Real-time temporal and spatial video analysis of table tennis', CVPRW 2020",
"url": "https://lab.osai.ai/"
},
"integrity": {
"input_sha256": "76257ccce922005b31663cbe0d7c27b36a6a95ea8efda3dfc89a7cce525976cb",
"target_sha256": "65ba189a5effe70692d7fdc9afc83b23c96d462fc1947856a28accc2f72e90a6"
},
"provenance": {
"engine": "v2v_taxonomy",
"engine_version": "1.0.0",
"pipeline_version": "v0.1.0-sample"
}
}This is a fixed-camera video of a table-tennis rally. Track the ball: produce a copy of the video in which the ball is marked in every frame with a red circle drawn at its current position. When the ball is occluded, off-screen, or otherwise not visible, draw no circle. Keep everything else in the frame identical to the input — same camera, scene, players and timing — and add only the ball marker.
广播多机位、球坐标来自 Label.csv(file,visibility,x,y)。一个 clip = 一段连续来回;标注密(~89%),圈几乎全程跟着球。红圈精度已抽帧放大核对(准确套在网球上)。
{
"schema_version": "v2v-metadata/2.0",
"task_id": "Task_14_ball_tracking",
"sample_id": "000000000",
"pipeline_id": "Task_14_ball_tracking",
"created_at": null,
"generation_mode": "video_to_video",
"transformation": {
"kind": "ball_localization_overlay",
"overlay": "target = input video with the ball circled each frame (frame-aligned)",
"marker": {
"shape": "circle",
"color_rgb": [
237,
28,
36
],
"radius_px": 14,
"stroke_px": 3,
"drawn_when": "ball visible (Label.csv visibility=1)",
"absent_when": "occluded/not visible -> no circle"
},
"clip": {
"name": "game1/Clip1",
"frames_marked": 114,
"frames_total": 128,
"note": "one TrackNet clip = one continuous broadcast rally shot"
},
"ball_track": {
"note": "per output-frame ball pixel (x,y) from TrackNet Label.csv; null = not visible",
"coord_space": "pixels in source 1280x720 (marker rescaled to 640-wide output)",
"per_frame": [
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},
"video": {
"num_frames_input": 128,
"num_frames_target": 128,
"fps": 25,
"resolution": [
640,
360
],
"duration_s": 5.12,
"source_media": "image_sequence",
"frame_source": "video_frames"
},
"prompt": {
"v2v_instruction": "This is a broadcast video of a tennis rally. Track the ball: produce a copy of the video in which the ball is marked in every frame with a red circle drawn at its current position. When the ball is occluded, off-screen, or not visible, draw no circle. Keep everything else in the frame identical to the input, and add only the ball marker."
},
"label_space": [
{
"phrase": "ball",
"marker": "red circle"
}
],
"domain": {
"field": "video_understanding",
"kind": "broadcast_video",
"attributes": {
"subtype": [
"ball_tracking",
"object_localization",
"tennis"
],
"sport": "tennis"
},
"source": {
"dataset": "TrackNet",
"clip": "game1/Clip1"
}
},
"source_dataset": {
"dataset": "TrackNet (tennis)",
"license": "unspecified (flag Hokin)",
"origin": "Huang et al., 'TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports', 2019",
"url": "https://nol.cs.nctu.edu.tw:234/open-source/TrackNet"
},
"integrity": {
"input_sha256": "08dcd585e2b406d4a342d8702a9d85de127ca1168a37ec44edf9dc5524a8d8cf",
"target_sha256": "fb18d375847e59451b2e229a312f7b0f80c6ef7fa84eecce88769cfde28a6c80"
},
"provenance": {
"engine": "v2v_taxonomy",
"engine_version": "1.0.0",
"pipeline_version": "v0.1.0-sample"
}
}This is a broadcast video of a tennis rally. Track the ball: produce a copy of the video in which the ball is marked in every frame with a red circle drawn at its current position. When the ball is occluded, off-screen, or not visible, draw no circle. Keep everything else in the frame identical to the input, and add only the ball marker.
{
"schema_version": "v2v-metadata/2.0",
"task_id": "Task_14_ball_tracking",
"sample_id": "000000001",
"pipeline_id": "Task_14_ball_tracking",
"created_at": null,
"generation_mode": "video_to_video",
"transformation": {
"kind": "ball_localization_overlay",
"overlay": "target = input video with the ball circled each frame (frame-aligned)",
"marker": {
"shape": "circle",
"color_rgb": [
237,
28,
36
],
"radius_px": 14,
"stroke_px": 3,
"drawn_when": "ball visible (Label.csv visibility=1)",
"absent_when": "occluded/not visible -> no circle"
},
"clip": {
"name": "game1/Clip2",
"frames_marked": 107,
"frames_total": 128,
"note": "one TrackNet clip = one continuous broadcast rally shot"
},
"ball_track": {
"note": "per output-frame ball pixel (x,y) from TrackNet Label.csv; null = not visible",
"coord_space": "pixels in source 1280x720 (marker rescaled to 640-wide output)",
"per_frame": [
[
721,
460
],
[
721,
456
],
[
724,
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},
"video": {
"num_frames_input": 128,
"num_frames_target": 128,
"fps": 25,
"resolution": [
640,
360
],
"duration_s": 5.12,
"source_media": "image_sequence",
"frame_source": "video_frames"
},
"prompt": {
"v2v_instruction": "This is a broadcast video of a tennis rally. Track the ball: produce a copy of the video in which the ball is marked in every frame with a red circle drawn at its current position. When the ball is occluded, off-screen, or not visible, draw no circle. Keep everything else in the frame identical to the input, and add only the ball marker."
},
"label_space": [
{
"phrase": "ball",
"marker": "red circle"
}
],
"domain": {
"field": "video_understanding",
"kind": "broadcast_video",
"attributes": {
"subtype": [
"ball_tracking",
"object_localization",
"tennis"
],
"sport": "tennis"
},
"source": {
"dataset": "TrackNet",
"clip": "game1/Clip2"
}
},
"source_dataset": {
"dataset": "TrackNet (tennis)",
"license": "unspecified (flag Hokin)",
"origin": "Huang et al., 'TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports', 2019",
"url": "https://nol.cs.nctu.edu.tw:234/open-source/TrackNet"
},
"integrity": {
"input_sha256": "fc076be445ed797f0943ff5a8b63cad90ec8f7e620579c1e5a2731e39e1f1584",
"target_sha256": "07c76975648a2306124f7a3e64aeac1864f076a70e58d927169e9c3cd9993768"
},
"provenance": {
"engine": "v2v_taxonomy",
"engine_version": "1.0.0",
"pipeline_version": "v0.1.0-sample"
}
}This is a broadcast video of a tennis rally. Track the ball: produce a copy of the video in which the ball is marked in every frame with a red circle drawn at its current position. When the ball is occluded, off-screen, or not visible, draw no circle. Keep everything else in the frame identical to the input, and add only the ball marker.
每个样品各有自己的一份 metadata.json 和 prompt.txt(见上方每个样品下的两个下拉框,框内可滚动)。批量后就是每个样品一套四件套、几百上千份。
ball_track、帧范围、sha256、来源 clip)。顶层结构照 Task_12 的 v2v-metadata/2.0,任务特有信息放独立块 transformation.ball_track。winnie-task14-final(us-east-2,无 GPU)→ 写
final_v1/New_Tasks/Task_14_ball_tracking/(乒乓+网球同一编号)→ 代码推 GitHub winnie_task14_pipeline → terminate。