Maximum Likelihood Estimation for Multiple Camera Target Tracking on Grassmann Tangent Subspace
- Submitting institution
-
University of East London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 14
- Type
- D - Journal article
- DOI
-
10.1109/TCYB.2016.2624309
- Title of journal
- IEEE Transactions on Cybernetics
- Article number
- -
- First page
- 77
- Volume
- 48
- Issue
- 1
- ISSN
- 2168-2267
- Open access status
- Deposit exception
- Month of publication
- -
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- 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
-
2
- Research group(s)
-
1 - Intelligent Systems
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The outcome of this paper is a non-iterative maximum likelihood estimation for multiple camera techniques. This work is significant because it reduces the complexity while delivering good performance and is not sensitive to elevation angle and observation error percentage values. This idea improves the performance of real-time surveillance systems dramatically. Based on this work, a MSc project was performed (Ali Vedad) and three journal papers in collaboration with University of Kingston and Shahid Beheshti University were published (10.1109/ACCESS.2019.2920477, 10.1109/JSEN.2018.2866429, 10.1504/IJSNET.2018.096215). This paper is cited by 10.1109/TASE.2018.2882641, 10.1109/ACCESS.2019.2939071 and 10.1051/jnwpu/20203820359.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -