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Multi-person Pose Estimation in Soccer Videos with Convolutional Neural Networks

Pose estimation is the problem of detecting poses of people in images, multiperson pose estimation is the problem of detecting poses of multiple persons in images. This thesis investigates multi-person pose estimation by applying the associative embedding method on images from soccer videos. Three models are compared, first a pre-trained model, second a fine-tuned model and third a model extended to handle image sequences. The pre-trained method performed well on soccer images and the fine-tuned model performed better then the pre-trained model. The image sequence model performed equally as the fine-tuned model but not better. This thesis concludes that the associative embedding model is a feasible option for pose estimation in soccer videos and should be further researched.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-148871
Date January 2018
CreatorsSkyttner, Axel
PublisherLinköpings universitet, Matematisk statistik, Linköpings universitet, Tekniska fakulteten, Signality
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

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