SportVision

Turn a short sports clip into an annotated video you can review and share. Choose jersey colors, follow the processing preview, and download the video and a report showing how much ball evidence was available.

Listed in Roboflow’s Community Plugins (merged listing).

Try the browser app · Use the Workflow blocks · Report a problem

Try the browser app

Requires Python 3.10 or newer.

pip install --upgrade "sportvision[app]"
sportvision app

Open http://localhost:8501 if your browser does not open automatically.

1. Upload an MP4, MOV, AVI, or MKV clip (up to 200 MB).
2. Choose the two jersey colors, or try automatic grouping. Start with 300 frames.
3. Select Analyze clip, then click a player box to choose their team or mark them Referee / ignore. The choice follows that track throughout the clip; Restore automatic assignment undoes it.
4. Click the colored timeline or move the frame slider to inspect the evidence. Gray segments explain why no possession was attributed. If the model missed a visible ball, turn on Mark a missed ball on this frame and click the ball. Use Correct possession for this frame to confirm the team from the video. These edits are labeled manual and affect only that frame.
5. Download the report. After a correction, select Build corrected video to export matching annotations without running detection again.

Track corrections do not reconnect identities after an ID switch. Review them where players overlap or leave the picture. The report keeps original team measurements alongside your changes.

Evaluation guide and provisional labels

Your video is processed on your computer. No account or API key is needed. Model weights download on first use. Processing may take longer than the clip.

For development from a cloned checkout, use pip install -e ".[app]".

Start with a short, steady basketball practice clip where both teams wear distinct shirts. This is the initial evaluation target, not a claim of validated basketball accuracy. Possession remains a pixel-proximity estimate: missing evidence is shown explicitly. Track IDs are not player identities.

Status: alpha. An experimental clip-review app and developer toolkit. Automatic field calibration, verified player identities, and validated match statistics are not available.

What works

• The pipeline combines COCO detection, ByteTrack from trackers, jersey-color clustering, annotations, and a nearest-player possession estimate.
• Speed, distance, heatmap, and homography utilities are available separately; the pipeline does not compute them automatically.
• Four optional Roboflow Workflow blocks expose filtering, team clustering, possession, and distance calculations.

Run a clip

From this repository:

pip install -e . ultralytics
python examples/demo.py --source match.mp4 --output analyzed.mp4 --model yolov8n.pt --max-frames 300

This writes analyzed.mp4 and analyzed.json, including processed-frame count, tracked-ball coverage, track count, and estimated possession. Model weights may download on first use. For RF-DETR, install "sportvision[inference]" and pass --model rfdetr-base.

For one frame:

import cv2
from sportvision.pipeline import SportVisionPipeline

frame = cv2.imread("match.jpg")
if frame is None:
    raise ValueError("Could not read match.jpg")
pipeline = SportVisionPipeline(model="yolov8n.pt")
result = pipeline.process_frame(frame)
cv2.imwrite("annotated.jpg", result["annotated_frame"])
print(result["stats"])

Install sportvision and ultralytics to use the Python example outside this repository. Missing detection backends and inference/tracking errors raise exceptions instead of producing apparently successful empty results.

Interpreting results

• Pretrained COCO models distinguish people and sports balls. They do not distinguish players, referees, goalkeepers, or spectators. Use footage with a clear view of the playing area.
• Team IDs are arbitrary color clusters, not home/away identities. Classification starts after enough players are visible; unknown teams are -1. Similar kits, shadows, and partial views can confuse it.
• Possession is the share of attributed observations within a pixel-distance threshold, not official possession or a share of all match time. {} means no estimate is available. Missing balls and unassigned observations do not count.
• Track IDs can change through occlusion; track count is not a count of unique players.
• Physical speed requires positions in meters and correct frame times. Distance uses the input coordinate units. A fixed homography is unsuitable after camera movement without recalibration. Heatmaps accept grid coordinates, not arbitrary image pixels.
• detect_every=N reuses old boxes for display between detections. fresh_detections identifies measured frames; skipped frames do not update team or possession estimates. This can miss fast ball movement.
• infer_size resizes the input before inference; smaller images can lose small balls. It does not guarantee a smaller model tensor or real-time performance. Measure throughput and accuracy on your own clip and hardware.

Roboflow Workflows Plugin

SportVision ships as a Roboflow Workflows plugin. Install with inference and activate:

pip install "sportvision[workflows]"
export WORKFLOWS_PLUGINS="sportvision.workflows"

This registers 4 blocks you can use in any Roboflow Workflow:

Example: Using blocks directly in Python

import cv2
import numpy as np
import supervision as sv

from sportvision.workflows.team_classifier.v1 import TeamClassifierBlockV1
from sportvision.workflows.possession_tracker.v1 import PossessionTrackerBlockV1
from sportvision.workflows.distance_calculator.v1 import DistanceCalculatorBlockV1
from sportvision.workflows.sports_detection_filter.v1 import SportsDetectionFilterBlockV1

# --- Filter COCO detections to sports classes ---
det_filter = SportsDetectionFilterBlockV1()
# Assume `raw_detections` comes from a COCO model (person=0, sports_ball=32)
result = det_filter.run(detections=raw_detections)
detections = result["detections"]  # now player=0, ball=1

# --- Classify players into teams ---
team_block = TeamClassifierBlockV1()
# `image` must have a .numpy_image attribute (or use WorkflowImageData)
# refit_every=10 refits KMeans every 10 frames (0 = fit once, default)
result = team_block.run(image=image, detections=detections, n_teams=2, refit_every=10)
detections = result["detections"]  # detections.data["team_id"] is now set

# --- Track possession ---
possession_block = PossessionTrackerBlockV1()
result = possession_block.run(
    detections=detections,
    ball_class_id=1,
    ball_proximity_threshold=100.0,
)
print(result["possession_stats"])   # {0: 0.6, 1: 0.4}
print(result["possessing_team"])    # 0
print(result["warning"])            # "" or warning if team_id missing

# --- Compute distances ---
# Supply detections after a tracking step that assigns persistent tracker_id values.
distance_block = DistanceCalculatorBlockV1()
# Optional: pass a 3x3 homography matrix for field-unit distances (e.g. meters)
result = distance_block.run(detections=detections, homography_matrix=[[0.01,0,0],[0,0.01,0],[0,0,1]])
print(result["detections"].data["distance"])  # cumulative distance per tracker

Workflow step fragment (tracking required)

The following is a step fragment, not a complete runnable workflow. Connect a tracker between teams and distance: the distance block requires persistent tracker_id values. Create one set of stateful blocks per video and process frames in order. Periodic team refitting can change assignments; use refit_every=0 for a fixed model.

{
  "steps": [
    {
      "type": "sportvision/sports_detection_filter@v1",
      "name": "filter",
      "detections": "$steps.model.predictions"
    },
    {
      "type": "sportvision/team_classifier@v1",
      "name": "teams",
      "image": "$inputs.image",
      "detections": "$steps.filter.detections",
      "n_teams": 2,
      "refit_every": 10
    },
    {
      "type": "sportvision/possession_tracker@v1",
      "name": "possession",
      "detections": "$steps.teams.detections",
      "ball_proximity_threshold": 100.0
    },
    {
      "type": "sportvision/distance_calculator@v1",
      "name": "distance",
      "detections": "$steps.tracker.detections",
      "homography_matrix": [[0.01,0,0],[0,0.01,0],[0,0,1]]
    }
  ]
}

Try it on Colab

Architecture

src/sportvision/
├── detection.py      # SportsDetector, wraps COCO detectors, maps to sports classes
├── tracking.py       # SportsTracker, ByteTrack via trackers
├── teams.py          # TeamClassifier, KMeans on HSV jersey histograms
├── homography.py     # FieldHomography, pixel to field coords
├── analytics/
│   ├── possession.py # PossessionTracker, nearest player to the ball per frame
│   ├── speed.py      # SpeedEstimator, displacement over time to km/h
│   ├── distance.py   # DistanceCalculator, cumulative path length
│   └── heatmap.py    # HeatmapGenerator, 2D histogram plus gaussian blur
├── annotators.py     # TeamColorAnnotator, StatsOverlayAnnotator, TrailAnnotator
├── pipeline.py       # SportVisionPipeline, orchestrates all modules
└── workflows/            # Roboflow Workflows plugin
    ├── _compat.py        # Inference compatibility shim
    ├── kinds.py          # Custom kind definitions
    ├── team_classifier/  # Team classification block
    ├── possession_tracker/   # Possession tracking block
    ├── distance_calculator/  # Distance calculation block
    └── sports_detection_filter/  # COCO→sports filter block

Sports Class IDs

Dependencies

Development

git clone https://github.com/MohibShaikh/sportvision.git
cd sportvision
pip install -e ".[all]"

# Tests
pytest tests/ -v

# Lint
ruff check src/ tests/ && ruff format --check src/ tests/

License

Apache-2.0

Analysis detail and limitations

The local app defaults to Detailed: YOLO runs at 1280 pixels with a 0.15 ball confidence threshold; player confidence remains 0.25. Quick uses 640 pixels and 0.25 for both. Detailed takes longer and may detect more false balls; its name is not an accuracy guarantee. Python and the example CLI retain Quick by default; pass analysis_mode="detailed" or --analysis-mode detailed.

Balls are retained as detector observations separately from player tracking. Their track ID is -1: they are not assigned a persistent identity. Unconfirmed player IDs are excluded from the unique-track count. The legacy report key frames_with_tracked_ball now counts frames with detected balls.

Detailed possession uses the distance from the ball center to a player's bounding box, allowing up to 15% of that player's height. If more than one player qualifies, it reports uncertainty. This handles scale and balls near hands more sensibly, but is still a heuristic: nearby players do not establish control of the ball. Quick retains the original 50-pixel center-distance rule. Reports record the chosen method, and manual corrections replay that same method.

See reproducible evaluation for the small diagnostic and what still needs independent validation before production accuracy claims.