Audio Assisted Robust Visual Tracking With Adaptive Particle Filtering
- Submitting institution
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Kingston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11-13-1347
- Type
- D - Journal article
- DOI
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10.1109/TMM.2014.2377515
- Title of journal
- IEEE Transactions on Multimedia
- Article number
- -
- First page
- 186
- Volume
- 17
- Issue
- -
- ISSN
- 1520-9210
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2015
- URL
-
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- Supplementary information
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
-
- Research group(s)
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-
- Citation count
- 39
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Speaker tracking in smart environments has recently become an increasingly important field of research, driven by applications such as automatic camera steering in video conferencing and individual speaker discrimination in multi-speaker environments. Indeed, the need for technologies to enable remote working has surged with the COVID-19 pandemic. The proposed work is significant in combining audio and video information for speaker tracking. With the rise in deep learning methods for tracking, the location and separation of subjects achieved in our work can be used as mid-level by deep learning recursive networks.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -