{"id":21714235,"url":"https://github.com/mingyi850/aiedgecam","last_synced_at":"2026-05-21T05:36:37.828Z","repository":{"id":39736283,"uuid":"198602234","full_name":"mingyi850/AiEdgeCam","owner":"mingyi850","description":"Demo developed for the Microsoft Technology Centre showcasing the deployment of AI models on edge devices","archived":false,"fork":false,"pushed_at":"2023-03-24T22:54:19.000Z","size":83933,"stargazers_count":0,"open_issues_count":3,"forks_count":0,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-01-25T17:32:28.783Z","etag":null,"topics":["ai","azure","edge","microsoft"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mingyi850.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-07-24T09:25:37.000Z","updated_at":"2024-04-20T15:29:14.000Z","dependencies_parsed_at":"2025-01-25T17:38:10.070Z","dependency_job_id":null,"html_url":"https://github.com/mingyi850/AiEdgeCam","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mingyi850%2FAiEdgeCam","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mingyi850%2FAiEdgeCam/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mingyi850%2FAiEdgeCam/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mingyi850%2FAiEdgeCam/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mingyi850","download_url":"https://codeload.github.com/mingyi850/AiEdgeCam/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244676451,"owners_count":20491828,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","azure","edge","microsoft"],"created_at":"2024-11-26T00:33:25.687Z","updated_at":"2026-05-21T05:36:32.809Z","avatar_url":"https://github.com/mingyi850.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Using Vision AI Dev Kit to build an Edge Computing Demo\n\nThis is a sample showing how to use Azure Machine Learning SDK and Azure IoT Edge to convert a model, build a container image, and deploy a model image to Vision AI Developer Kit in Visual Studio Code.\n\n![Architecture Diagram](Images/ArchitectureDiagram.jpg)\n\n## Introduction\n\nThe Vision AI Dev Kit is a camera developed by Qualcomm which includes its proprietary Neural Processing Engine, the Snapdragon Neural Processing Engine (SNPE). It allows users to develop and showcase use-cases for IOT Edge solutions which are integral in environments or situations which require:\n\n1. \tQuick response time and low latency\n2.\tHigh volumes of network traffic and cloud processing\n3. \tHigh resilience despite low internet connectivity\n\nThis guide will teach you how to setup a demo which features:\n\n### 1.  Workplace Safety AI\n\nThis demo imagines a workspace environment where a workspace is monitored by AI Cameras to detect safety violations. When a user is caught entering the work-zone without a helmet, the camera detects it, his picture is taken, and a notification is sent to a supervisor.\n\n* \tData and AI\n\n    This demo demonstrates the camera’s AI inferencing capabilities. It can be used as a tool to drive conversations surrounding AI, and the real-world value it can provide. It also demonstrates the feasibility of video analytics as a solution in areas with low connectivity, or as a measure to reduce bandwidth usage.\n* \tPower Platform\n\n    This demo can also be used to show the ease of use of PowerApps to build front-end applications and interfaces from existing data (using data collected from the camera stored in the CDS entity). \n\n### 2. \t“Plug and Play” Module switching\nThis demo shows the capabilities of IOT Hub in the management and deployment of modules to Edge devices, addressing concerns with the scalability of Edge solutions. Clients will be able to see a visual representation of how a module is deployed at scale to several devices via the azure portal.  \n\n\n# Implementation Details:\nSee the [Setup Guide and Documentation](\"Documentation.docx\") for step by step implementation guide\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmingyi850%2Faiedgecam","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmingyi850%2Faiedgecam","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmingyi850%2Faiedgecam/lists"}