Computer Vision from the Edge to the Cloud done easy

Run powerful cloud AI without streaming endless video. This architecture uses smart cameras to trigger analysis on a single snapshot, slashing costs.

#1about 6 minutes

Defining computer vision and its real-world applications

Computer vision enables computers to understand digital images and videos, with applications in retail, public safety, traffic monitoring, and smart cities.

#2about 2 minutes

Understanding the key components of a vision system

A typical computer vision architecture includes cameras for recording, storage for footage, a machine learning pipeline for processing, and a dashboard for results.

#3about 3 minutes

Exploring the features of Cisco Meraki IP cameras

Cisco Meraki cameras are cloud-managed devices with on-board storage and processing for detecting people, vehicles, and audio events like fire alarms.

#4about 2 minutes

Integrating cameras using APIs, MQTT, and RTSP streams

Meraki cameras offer multiple integration points including a REST API, webhooks for cloud events, local MQTT for real-time triggers, and RTSP for video streaming.

#5about 6 minutes

Demoing real-time event detection and analysis

A live demonstration shows how a camera's local MQTT broker can trigger events for person detection in a zone and audio alarm recognition.

#6about 5 minutes

Designing an efficient event-driven vision architecture

Use on-camera analytics and MQTT triggers to send a single snapshot to a cloud vision API for analysis, reducing bandwidth and processing costs.

#7about 2 minutes

Comparing pre-trained models from AWS, Azure, and GCP

A comparison of the pre-trained computer vision models and pricing tiers available on AWS Rekognition, Azure Computer Vision, and Google Cloud Vision API.

#8about 1 minute

Deciding between pre-trained and custom vision models

While pre-trained models are easy to use, building a custom model with your own dataset is necessary for highly specific detection tasks.

#9about 3 minutes

Showcasing computer vision project examples

Practical examples demonstrate architectures for detecting face masks, capturing license plates, and using door sensors to trigger snapshots for analysis.

#10about 18 minutes

Answering audience questions on practical implementation

The Q&A session covers topics like using cameras for home security, filtering out pets from alerts, and the challenges of creating custom models for specific tasks.