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Mid-level Summarization in First-Person-View Videos

Project Description

Traditional computer vision systems suffer from large occlusions and limited field of views because of the nature of the fixed capturing devices (like fixed surveillance cameras). Wearable devices (like Google Glass or Narrative clip) are more flexible and get unobstructed and closer views of objects or people. These devices are becoming cheaper and widespread on the market. In this project, the candidate will develop new algorithms for summarizing life-logged video data captured using wearable devices. We will try to summarize mostly mid-level daily activities (like preparing meals, commute to office, social drinking, etc.), which should take into account both relevance/saliency and diversity. By relevance we mean that the retrieved data should have similar content in nature (for example, seeing the landmarks in our daily commute to the office within a month of video data). Diversity means that the retrieved data should be from most of the (different) days/times of the database. The candidate will develop new machine/deep learning and computer vision algorithms to address the above problem of video summarization, which would be publishable in relevant workshops, reputed conferences and high impact journals.

Funding Notes

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

How good is research at Kingston University in Computer Science and Informatics?

FTE Category A staff submitted: 10.20

Research output data provided by the Research Excellence Framework (REF)

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