Sep
TL;DR – how to use cross-platform tech in Docker to build arm64 solutions on an x86 platform At Seechange, we want our partners and customers to be able to integrate easily with our platform. Models can be developed elsewhere and deployed by SeeWare to deliver inference data in our secure, scalable environment. One of the […]
The newly formed partnership between SeeChange and Ocucon sees the release of if its first product targeted at the loss prevention section, Spill Detect. Designed to address the poor perception of risk by humans, a significant and common contributory factor to slip, trip and fall incidents, Spill Detect removes them from the reporting loop through […]
By Eleftherios Fanioudakis and Nimesh Patel Introduction This is part 1 of a 3 part series which details the technical journey SeeLabs have been on, to solve the following challenge we were presented with. The parts are as follows: Part 1: Retail Loss at Checkouts: Introduction to the Problem and the Detector-Classifier System Part 2: […]
SeeChange enters into loss prevention partnership. SeeChange and Ocucon team up to develop innovative solutions for loss prevention.
Jan
This post is the first in a series of “security” blogs which dive into the thought process behind security and privacy within SeeChange.
Oct
Recorded on October 13th 2020 Dr. David McCaffrey introduces the SeeWare platform and how it is measuring live occupancy at The Landing in Manchester UK. This talk was part of the Digital Leaders Week 2020 virtual conference. SeeChange is leading the way in post covid-19 technology & computer vision models to recognise space occupancy and […]
Oct
We challenged the SeeChange Labs team to create a local IoT device to provide notifications when stock levels get low on shelves in retail stores.
Oct
Computer Vision is all about objects. The optimum would be to see the complete outline and content of a single object to be identified against a contrasting, plain background. However, that’s not the real world. We need to achieve a high level of confidence where the object is partially obscured, at an angle, badly lit, […]