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Project Database
Project Reference: ITP/034/22LP
Project Title: Video Analytics for Tracking Cyclists in Track Cycling Race from Dynamic Pan-Tilt-Zoom Scenes
Hosting Institution: LSCM R&D Centre (LSCM)
Abstract: This seed project aims to digitalize track cycling race data from pan-tilt-zoom filmed
videos for paving the way to deepen data-driven sports analytics for strengthening the
support for Hong Kong Cycling Team to derive training and game strategies. The R&D
methodology involves the integral use of techniques in machine learning, computer
vision, and information science. A cyclist detector viewed from multiple angles will be
trained through a deep learning mechanism. Relevant computer vision algorithms will be
used to track detected cyclists across the pan-tilt-zoom (PTZ) scenes embedding with
moving background and encountering multiple viewing planes. Information science
techniques will be used to keep track of the spatiotemporal relationships between each of
the local PTZ scenes to a re-constructed global race track. Together, cyclists’
spatiotemporal positions to the race track can be obtained for analyzing their interactions
during a game, which is the insight for strategizing game plans. The R&D work on
tracking moving object with a moving PTZ camera, instead of a static camera, may lead
to more opportunities to apply video analytics over visual content from drones or other
PTZ applications for transportation, manufacturing, logistics, surveillance, as well as
sports industries.
Project Coordinator: Dr To Bun NG
Approved Funding Amount: HK$ 2.79 M
Project Period: 1 Dec 2022 - 29 Feb 2024
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