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  3. DashLoc
__22 days agoContributorJudging locked: Event build

DashLoc

Helps reconstruct a journey from pure dashcam footage, for law enforcement, claim auditing and personal logging

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Project description
What we built: GeoTrace works out where a dashcam clip was recorded from the video alone, with no GPS and no metadata. It reconstructs six 15-minute Toronto trips on a map, to a median of 10 m when we hide a sign and predict where the car was. It can also check a claim, such as “was this recorded at X?”: a made-up location came back 557 km off. We also built TripScore, which gives each trip an explainable driving-risk score from the same footage.How it works: Cosmos3-Reason reads street signs, business names, tram tracks, motion and turns from every 5-second segment. An anchor-chain solver then matches the sign reads to the OpenStreetMap road network and rejects misreads that don’t fit the distance and timing. Optical flow measures turns independently, and a second model re-checks each sign from the clip’s frames. An independent business-name check agrees for 38 of 73 names within 300 m. The app is deployed on the team cluster.Sponsor tech:
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NVIDIA:
Cosmos3-Reason powers the sign and motion reading, plus the fault review in TripScore. YOLO11 detections drive TripScore’s tracking, and Nemotron-3.5-Super-VL-120B is the second opinion.
CoreWeave and Weights & Biases:
the Cosmos and YOLO endpoints run on the CoreWeave GPUs, and we called Nemotron through W&B Serverless Inference.
VAST Data:
we built on the pre-ingested index, S3 segments and detection sidecars through the VSS API, and deployed to the team cluster with the challenge’s skill.
Project links
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Tools used
  • VAST Data logoVAST Data

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  • CoreWeave (Weights & Biases) logoCoreWeave (Weights & Biases)

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  • NVIDIA logoNVIDIA

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  • SpaceXAI (Cursor) logoSpaceXAI (Cursor)

    Event resource · Sign up for Cursor

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