OpenHoopVision
AI-powered basketball video analysis for amateur and youth games, with player tracking, event detection, and structured data export.
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GITHUB · launched Oct 12, 2026 · 9 upvotes (top 98%)
- Media & entertainment
- Analytics & BI
- Prosumer
- AI-native
- Hobby / open-source project
Similar launches
See all 1354 similar launches →Could you build this?
Partial While basic computer vision pipelines with off-the-shelf YOLO and DeepSORT can be vibe-coded, reliably tracking players with jersey/court mapping and detecting nuanced basketball events in amateur footage requires customized model training and tracking logic.
What it would actually take: The architecture needs a multi-stage computer vision pipeline: multi-object tracking (e.g., ByteTrack/BoT-SORT) fine-tuned on amateur basketball video, court homography estimation to project 2D camera coordinates into 2D court space, and temporal action detection models (like SlowFast or transformer-based video classifiers) to recognize rebounds, assists, and shot attempts. Fine-tuning these vision models requires high-quality annotated amateur basketball datasets and expertise in spatial-temporal video analysis.
Competitors
Other products that read as similar to this one : 1354 launches clear the similarity bar, closest 8 shown.
Attention rank: #1341 of 1355 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 348 days after the earliest competitor.
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- Dragoneye: Production video detection models in <5 minutes · yc · 2025-11-25 · 11 upvotes · similarity 0.54
Other launches for this product
- No other launches for this product.
More details
- External ID
- 1415361730
- Company
- n/a
- Product
- OpenHoopVision
- Website domain
- github.com
- Cohort
- n/a
- Upvotes percentile
- 0.02
- All tags
- n/a
- AI summary
- ai basketball video analysis and player tracking for coaches and teams
- Manually corrected
- No