# Cloud Vision AI Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/cloud-vision-ai
> Markdown URL: https://aitinkerers.org/technologies/cloud-vision-ai.md
> Technology record last updated: 2026-02-24T13:28:54Z
> Generated: 2026-09-20T14:48:00Z

Cloud Vision AI delivers immediate, high-accuracy visual intelligence: automate image labeling, object detection, and text extraction at scale.

This is Google Cloud's core computer vision solution, providing pre-trained models via a REST/RPC API (Cloud Vision API) and custom model training via Vertex AI. It processes images for deep, structured insights: OCR (Optical Character Recognition), face analysis, landmark detection, and explicit content filtering (SafeSearch). Retail clients, for example, use it for automated inventory management and product search; media companies leverage it for cataloging millions of digital assets. You get 1,000 units of features free monthly, then pay per use (e.g., $0.035/count for Product Search metadata).

- Official technology site: https://cloud.google.com/vision-ai
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Vibe✓ : Agentic Street-Level Stress Detection with Gemini and Vertex AI](https://nyc.aitinkerers.org/talks/rsvp_jmM5cVIBnT4)

This talk walks through the development of a real-time, cloud-native system for analyzing stress conditions across New York City using live CCTV footage. The system built using Angular and Google Cloud’s AI stack: Vertex AI, Gemini, BigQuery ML, together with Firebase app hosting, captures 940 concurrent camera streams and performs context-aware analysis through a Gemini-powered multimodal agent. Each camera is mapped to dynamically generated borough zones based on Voronoi tessellation, enabling location-specific metrics. The project spans five phases: 1. Multimodal vision analysis using Gemini for stress detection, with adaptive confidence thresholds. 2. A hybrid ML pipeline using Vertex AI and rule-based logic to classify high-risk conditions with 85%+ accuracy. 3. Time-series forecasting with BigQuery ML (ARIMA_PLUS) to detect seasonal patterns and enable pedestrian-aware predictive routing. 4. Event-driven, serverless infrastructure using Firebase Functions to support six-tier adaptive monitoring windows. 5. Live deployment with Firebase Hosting and GitHub Pages to ensure demo reliability and real-time updates. The demo includes a live analytics dashboard showing violation forecasts and real-time confidence scores for each camera zone. Each camera stream is sampled adaptively based on stress levels, and the backend is built for continuous training and edge-case reinforcement.

- Event context: Building AI Agents with Google Cloud AI — 2025-06-25 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_jmM5cVIBnT4

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