GCP / TensorFlow / Unity

Huey aims to remove unconscious bias from judges in surfing.

Surfers of a particular age, gender, status looks or even nationality might be judged beyond their performance.

This tool creates metrics for evaluating the skill of a surfer on aspects that are factual. Judges can compare exact values on how high a surfer rose from a wave, how vertical they were and how much they rotated – and close the gaps of uncertainty our unconscious biases can hide in.

The tool itself is the first of its kind, and has been built on the TensorFlow platform with training data gathered specifically for this project. Huey is the result of a small group of passionate creatives, engineers and surfers with a limited budget and resources. It ultimately seeks to erase bias from sports, one wave at a time.

While Project Huey was developed for surf competition judging, the scalability into other, and mostly less complex sports, is the logical next step. Besides the implied long-term outcome of freeing sports judging from biases, Huey aims to start a bigger conversation. A conversation about what humans are good at, what machines are good at - and how we can maximise these strengths.

PARTNERS

Google / Stab Magazine

AWARDS

One Show
Merit; Experimental Internal projects / R&D

SPIKES ASIA
Innovation Spike

Architecture - GCE, TensorFlow model training on GCP, real-time inference at the edge, Unity visualisation, broadcast integration.

Platforms - GCP (Vertex AI, Compute Engine), Unity, IP Cameras.

Key Services - Vertex AI, TensorRT, Cloud Functions/Cloud Run, IP Camera SDK/API, Edge Functions (for pre-processing), OpenCV, Scipy,

Languages/Tech - Python (TensorFlow), C# (Unity)