I will computer vision ai mobile app for sports player detect bytettrack deepsort yolo

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Full Stack AI ML NLP Engineer Data Scientist Computer Vision Full-Stack Developer & AI Engineer with 11+ years experience. Python, React, Django, Java, C++, C#, Spring Boot, AWS/Azure/GCP expert. Int...
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Want a mobile app that detects and tracks players in sports footage with production-grade accuracy? I develop computer vision mobile apps that combine YOLO detection with ByteTrack / DeepSORT multi-object tracking to deliver stable player IDs, trajectory analysis, and exportable analytics. The app supports live camera or video input, overlays bounding boxes and player IDs, computes speed/trajectory metrics, and outputs structured data (CSV/JSON or live API). Ideal for coaches, analysts, broadcasters, and sports tech teams working on football, basketball, tennis, athletics, climbing, or combat sports.


Services We Offer (quick list)

  • YOLO model integration for player & object detection
  • ByteTrack or DeepSORT for multi-player consistent ID tracking
  • Pose estimation integration (MediaPipe/OpenPose)
  • Mobile optimization (TFLite / Core ML conversion & quantization)
  • Live camera overlay: bounding boxes, IDs, confidence, metrics
  • Player trajectory, speed, and heatmap generation
  • Export: CSV / JSON logs, annotated video, real-time API/WebSocket
  • Multi-camera or streamed input (optional)
  • Backend streaming endpoint (FastAPI) and dashboard hooks
  • Dataset labeling & custom model fine-tuning

Contact Us

API:

Microsoft Computer Vision AI

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Amazon Rekognition

Experiencia:

Procesamiento de imágenes

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Clasificación

Lenguaje de programación:

Python

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R

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Colab

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NoSQL

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MLflow

Herramientas:

Jupyter Notebook

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opencv

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TensorFlow

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Excel

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MLflow

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Colab

Marcos:

Scikit-learn

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DeepPy

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Google ML Kit

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PyTorch

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Panda