Computer Vision · Researcher
Soccer Vision Research.
Modular research framework that orchestrates four pretrained vision models (RF-DETR, SAM2, SigLIP, ResNet) into one configurable soccer-analysis pipeline with selectable fusion strategies.
- Role
- Researcher
- When
- 2024
- Stack
- Python, PyTorch, RF-DETR, SAM2
- Scale
- 4 models orchestrated
RF-DETR · SAM2 · SigLIP4 modelsorchestrated
7fusion strategies
3execution modes
YAMLconfig-driven
The problem
Combining several vision models for soccer analysis usually means hard-wiring one fixed pipeline. The goal here was a modular framework where detection, segmentation, identification, and classification models are swappable and configurable, so model combinations can be A/B tested without rewriting code.
What it does
- An orchestration layer (ModelPipeline) that runs RF-DETR detection, SAM2 segmentation and tracking, SigLIP zero-shot identification, and ResNet jersey classification.
- Three execution modes (sequential, parallel, adaptive) plus a result-fusion layer with seven selectable strategies.
- YAML-driven, schema-validated config per model, with a model registry and manager so presets are configuration, not code.
Impact
- Swap models or presets through config alone, enabling rapid A/B testing of model combinations.
- SigLIP text-image matching identifies players and teams zero-shot, without training on a specific roster.
- A documented framework with per-model demos and a config system built for research reproducibility.