Recently, ONVIF the leading global standardization initiative for IP-based physical security products, hosted the ONVIF Open Source Spotlight Challenge, a technology competition to develop open source applications for innovative IP video streaming applications to help resolve global security issues. I signed up the competition and built “cam X” a proof of concept iOS project written in Swift to demonstrate how Deep Learning, IPFS and Ethereum technologies can be applied together in practice to equip the normal IP cameras with superpowers. You can get the project from GitHub.

What issues the current security market is facing

Video Analytics is complex to configure, resources consuming and single-tasking in general.

Traditional Video Management Solution data storage space is finite & expensive, backup & maintenance are extremely painful and has single point failure risk.

Valuable data mostly stores centralized without encryption, under the risk of being tampered and has privacy concern.

What is cam X’s magic solution

All you need is a cellphone! cam X provides a serverless solution equips with two general purpose object detection engines Tiny Yolo which detects 20 object types in real-time and Yolo 2 a bit slower version which detects 80 object types and has better accuracy. cam X turns any IP camera to an AI beast within the snap of a finger. IPFS & Ethereum is a seamless solution to keep data encrypted and place the immutable fingerprints into blockchain transactions.

How cam X works

Step 1: Camera Management

App initial view is a camera list. There’re three sections: ONVIF, IP and Mobile. ONVIF contains some demo cameras provided by competition sponsors use the latest Profile T protocol. IP cameras are network cameras use the http or rtsp protocol. Mobile is the iOS device build-in camera. I prepopulated some default cameras for each section.