<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-22T07:57:45Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/FD6256D0-B62F-4A67-90AA-3B29D88A7B88" ns1:id="FD6256D0-B62F-4A67-90AA-3B29D88A7B88"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/10650ED8-FFB8-4087-A12C-5021120BE506" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/E62A3B0D-E413-4626-9E04-1C173C2F7272" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/E62A3B0D-E413-4626-9E04-1C173C2F7272" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/606DCBC6-9C43-45A0-9C1C-084BCA6E6EDB" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2016-10-31T00:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/8E250673-8B17-4F7D-B34E-75389BF18815" ns1:rel="FUND" ns1:start="2015-11-01T00:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">710752</ns2:identifier></ns2:identifiers><ns2:title>Alchera Mixed Mode Bicycle Detection</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>GRD Proof of Concept</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>Vivacity Labs is a leading Intelligent Transport Systems company that designs and operates intelligent camera-based platforms to interpret, predict and react to movements using computer vision technology.
Following the success of this platform in tracking pedestrian and vehicle movement in public places such as stations, we now want to develop an advanced system to specifically identify and differentiate cyclists in mixed mode traffic, to ultimately reduce the number of cycle related road accidents.

Although systems exist to track cars, difficulties in identification, differentiation and automation means no one has managed to achieve this for cyclists on a low-cost platform before. Our first step to doing this is the design and development of a series of algorithms to detect cyclist’s unique characteristics using computer vision and machine learning technology.</ns2:abstractText></ns2:project>