Activity detection, analysis and recognition are related to several important areas in machine perception research and applications. Goals include automatic analysis or interpretation of ongoing events, detection of pre-specified events and their context from multimedia data, and recognition and prediction of actions of one or more agents from a series of observations. There are several applications demanding such techniques, e.g., surveillance systems, patient monitoring systems, sport training-assistant systems, and a variety of systems that involve interactions between persons and electronic devices such as human-computer interfaces. In general, these applications require recognition of high-level activities, often composed of multiple simple actions of clients, with cues from single modal, e.g., audio stream or video clips (image sequences), or information fusion from multi-modal data, e.g., different types of behaviour data collected in mobile devices.
In general, the project aims developing effective techniques for automatic activity detection, analysis, recognition with flexibility applicable to different mono-modality, e.g., video clips or audio stream, or multi-modality by using cues from different information sources. Several issues are going to be investigated including low-level feature extraction, intermediate-level descriptors, high-level semantic analysis/representation and knowledge compression required by mobile device in real applications. In addition, temporal information processing and context-aware computing techniques are also core issues to be studied in this project. While this is a generic project description, the specific project will be well-defined with the perspective yet self-motivated student???s input. As an artefact of such a project, normally, a prototype with some clear scenarios or requirements needs to be developed to demonstrate the effectiveness and flexibility along with developing novel approaches. While the relevant fundamental research is expected to be conducted, the project is suitable for one who has a clear targeted application area in mind.
In order take this project, it is essential to have good background knowledge in both machine learning and image/speech signal processing as well as excellent programming skills. If you are interested in this project, please first visit my research student page: http://staff.cs.manchester.ac.uk/~kechen/ for the required materials and information prior to contacting me.
Candidates who have been offered a place for PhD study in the School of Computer Science may be considered for funding by the School. Each year around 20 new PhD students are awarded funding via the School. Further details on School funding can be found at: http://www.cs.manchester.ac.uk/study/postgraduate-research/programmes/phd/funding/school-studentships/.
In addition, exceptional students may be considered for the President's Doctoral Scholar Award and the Dean's Award. Further details on these opportunities can be found at: http://www.eps.manchester.ac.uk/our-research/funding/.
Supervisor's Webpage: http://www.cs.man.ac.uk/~kechen/.
How good is research at University of Manchester in Computer Science and Informatics?
FTE Category A staff submitted: 44.86
Research output data provided by the Research Excellence Framework (REF)
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