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  Human Motion Analysis using Computer Vision and Deep Learning

   Faculty of Engineering, Computing and the Environment

   Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Analysis of human motion is implicitly or explicitly required in many areas such as healthcare, sports, video surveillance, body-based user interfaces and computer games and animation. Currently, such analysis is usually performed by experts such as medical doctors, physiotherapists and sports scientists, and automatic interpretation of human movements is feasible in only a few cases.

This project will focus on how to process video streams to automate human motion analysis. Appropriate Computer Vision and Machine Learning methods for pose estimation and tracking, gait analysis, motion assessment and activity recognition will be investigated.

Candidates should have appropriate academic qualifications (first or upper second class honours, and preferably MSc degree) in Computer Science, Engineering, Mathematics, Physics or other relevant area, strong background in programming and desire to become experts in Computer Vision and Machine Deep Learning.

Qualified applicants are encouraged to contact Professor Dimitrios Makris () to informally discuss the project.

Supervisor’s profile:

Google Scholar profile:

Computer Science (8)

Funding Notes

There is no funding for this project: applications can only be considered from self-funded candidates


[1] Georgios Mastorakis, Tim Ellis, and Dimitrios Makris. "Fall Detection without People: A Simulation Approach Tackling Video Data Scarcity." Expert Systems with Applications (2018).
[2] Bloom, Victoria, Argyriou, Vasileios and Makris, Dimitrios, Linear latent low dimensional space for online early action recognition and prediction. Pattern Recognition, ISSN (print) 0031-3203 (Epub Ahead of Print), 2017
[3] Rodriguez, Mario, Orrite, Carlos, Medrano, Carlos and Makris, Dimitrios (2016) One-shot learning of human activity with an MAP adapted GMM and simplex-HMM. IEEE Transactions on Cybernetics, 47(7), pp. 1769-1780. ISSN (print) 2168-2267
[4] Rodriguez, Mario, Orrite, Carlos, Medrano, Carlos and Makris, Dimitrios, A time flexible kernel framework for video-based activity recognition. Image and Vision Computing, 48-49, pp. 26-36. ISSN (print) 0262-8856, 2016
[5] Bloom, Victoria, Argyriou, Vasileios and Makris, Dimitrios (2016) Hierarchical transfer learning for online recognition of compound actions. Computer Vision and Image Understanding, 144, pp. 62-72. ISSN (print) 1077-3142
[6] Martínez del Rincón, J, Lewandowski, M., Nebel, J.C. and Makris, D. (2014) Generalised Laplacian Eigenmaps for Modelling and Tracking Human Motions. IEEE Transactions on Cybernetics, 44(9), pp. 1646-1660. ISSN (print) 2168-2267, 2014
[7] Lewandowski, Michal, Makris, Dimitrios, Velastin, Sergio A and Nebel, Jean-Christophe, Structural Laplacian eigenmaps for modeling sets of multivariate sequences. IEEE Transactions on Cybernetics, 44(6), pp. 936-949. ISSN (print) 2168-2267, 2014

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