About the Project
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 or 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 Dr Dimitrios Makris ([email protected]) to informally discuss the project.
Google Scholar profile:
 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
 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
 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
 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
 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
 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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