Designed specifically for sports analytics, this model recognizes players alongside gear like balls, rackets, helmets, and other common sports equipment.

It returns structured bounding boxes with confidence scores and class labels—so you can power performance analysis, broadcasting enhancements, and fan engagement tools without building a custom vision stack.

Use it on images, recorded video, or live streams. No custom training required.

Optimized for:

• Multi-class detection (people + sports equipment)
• Fast-moving scenes + motion-heavy footage
• Frame-by-frame results for video
• Cloud or On-Prem deployment
• Rapid setup for prototype → production
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Why This Model Exists

In sports footage, “person detected” is only step one.

What teams actually need is context:

• Which equipment is in play?
• Where is it relative to the player?
• What changed frame-to-frame that indicates an action?

Sports teams often try to stitch this together with separate models (player detection + ball detection + sport-specific logic). That creates friction fast:

• Different models behave differently under motion blur and occlusion
• Small objects (like balls) are difficult to keep stable
• Outputs can be inconsistent across camera angles, lighting, and broadcast overlays
• Building a sport-specific pipeline slows experimentation

This model exists to provide a single baseline:
players + common sports equipment detected in one pass, with a unified output schema—so you can build tracking, analytics, and broadcast features faster.

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Key Capabilities

Input Types

• Single images
• Video files
• RTSP / livestream feeds
• Webcam / IP camera streams

Output

• JSON with bounding boxes
• Confidence scores
• Class labels (person + equipment types)
• Frame-level detections (for video/streams)

Setup

• Create account
• Get API key
• Send media
• Receive detections instantly

No training. No labeling. No tuning.

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Example Output

{
  "objects": [
    {
      "category": "person",
      "classLabel": "person",
      "confidence": 0.962,
      "x": 604.8,
      "y": 188.3,
      "width": 412.5,
      "height": 862.7
    },
    {
      "category": "equipment",
      "classLabel": "tennis_racket",
      "confidence": 0.931,
      "x": 790.2,
      "y": 352.6,
      "width": 284.4,
      "height": 318.9
    },
    {
      "category": "equipment",
      "classLabel": "ball",
      "confidence": 0.781,
      "x": 972.1,
      "y": 410.8,
      "width": 26.4,
      "height": 26.1
    }
  ],
  "source_width": 1920,
  "source_height": 1080
}

(Update equipment labels to match your model’s taxonomy—ex: tennis_racket vs racket, ball vs tennis_ball, helmet, bat, stick, etc.)

Practical Use Cases

Performance & Coaching Analytics

• Player + equipment proximity metrics
• Shot / swing / strike event cues (with tracking + rules)
• Drill analysis from practice footage
• Automated clip filtering (“find frames with racket + ball”)

Sports Broadcasting Enhancements

• Real-time overlays (player + equipment highlights)
• Automated replays triggered by equipment motion (with downstream logic)
• Instant tagging for highlight creation workflows
• Metadata generation for search and replay systems

Fan Engagement Tools

• Interactive “tap to learn” overlays
• Live stats triggers based on equipment presence
• Auto-tagging for social clip generation
• Second-screen experiences driven by on-screen context

Sports Tech & Computer Vision Products

• MVP baseline detection for sport-specific apps
• Faster dataset curation (“only clips with helmets/bats/balls”)
• Equipment-aware tracking inputs for downstream pipelines

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Why This Output Matters

Equipment detection is what turns “there’s a player” into “something is happening.”

With players + equipment in the same output, you can derive:

• Interaction likelihood (player + racket overlap)
• Zone events (ball enters court ROI)
• Action cues (equipment speed/trajectory when tracked)
• Contextual tagging (sport type, play type, training type)

All from bounding boxes—without starting from scratch.

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Deployment Options

EyePop Cloud

• Scalable
• Managed infrastructure
• Best for web apps + fast iteration
  

On-Premise Runtime

• Keep video inside your network
• Lower latency options
• Works with GPU or CPU environments
• Ideal for regulated or sensitive environments
  
  

Who This Is For

• Sports tech teams building analytics products
• Broadcasters and production teams enhancing live feeds
• Teams creating fan engagement and interactive experiences
• Anyone who needs player + equipment context fast, without custom training