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Ismagilov, Timur; Ferrarini, Bruno; Milford, Michael; Tuyen, Nguyen Tan Viet; Ramchurn, Sarvapali D.; Ehsan, Shoaib
On motion blur and deblurring in visual place recognition Journal Article
In: IEEE Robotics and Automation Letters, vol. 10, no. 5, pp. 4746–4753, 2025.
Abstract | Links | BibTeX | Tags: data sets for robotic vision, Localization, Vision-Based Navigation
@article{soton507348,
title = {On motion blur and deblurring in visual place recognition},
author = {Timur Ismagilov and Bruno Ferrarini and Michael Milford and Nguyen Tan Viet Tuyen and Sarvapali D. Ramchurn and Shoaib Ehsan},
url = {https://eprints.soton.ac.uk/507348/},
year = {2025},
date = {2025-03-01},
journal = {IEEE Robotics and Automation Letters},
volume = {10},
number = {5},
pages = {4746–4753},
abstract = {ensuremath<pensuremath>Visual Place Recognition (VPR) in mobile robotics enables robots to localize themselves by recognizing previously visited locations using visual data. While the reliability of VPR methods has been extensively studied under conditions such as changes in illumination, season, weather and viewpoint, the impact of motion blur is relatively unexplored despite its relevance not only in rapid motion scenarios but also in low-light conditions where longer exposure times are necessary. Similarly, the role of image deblurring in enhancing VPR performance under motion blur has received limited attention so far. This letter bridges these gaps by introducing a new benchmark designed to evaluate VPR performance under the influence of motion blur and image deblurring. The benchmark includes three datasets that encompass a wide range of motion blur intensities, providing a comprehensive platform for analysis. Experimental results with several well-established VPR and image deblurring methods provide new insights into the effects of motion blur and the potential improvements achieved through deblurring. Building on these findings, the letter proposes adaptive deblurring strategies for VPR, designed to effectively manage motion blur in dynamic, real-world scenarios.ensuremath</pensuremath>},
keywords = {data sets for robotic vision, Localization, Vision-Based Navigation},
pubstate = {published},
tppubtype = {article}
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Ismagilov, Timur; Ferrarini, Bruno; Milford, Michael; Tuyen, Nguyen Tan Viet; Ramchurn, Sarvapali D.; Ehsan, Shoaib
On motion blur and deblurring in visual place recognition Journal Article
In: IEEE Robotics and Automation Letters, vol. 10, no. 5, pp. 4746–4753, 2025.
@article{soton507348,
title = {On motion blur and deblurring in visual place recognition},
author = {Timur Ismagilov and Bruno Ferrarini and Michael Milford and Nguyen Tan Viet Tuyen and Sarvapali D. Ramchurn and Shoaib Ehsan},
url = {https://eprints.soton.ac.uk/507348/},
year = {2025},
date = {2025-03-01},
journal = {IEEE Robotics and Automation Letters},
volume = {10},
number = {5},
pages = {4746–4753},
abstract = {ensuremath<pensuremath>Visual Place Recognition (VPR) in mobile robotics enables robots to localize themselves by recognizing previously visited locations using visual data. While the reliability of VPR methods has been extensively studied under conditions such as changes in illumination, season, weather and viewpoint, the impact of motion blur is relatively unexplored despite its relevance not only in rapid motion scenarios but also in low-light conditions where longer exposure times are necessary. Similarly, the role of image deblurring in enhancing VPR performance under motion blur has received limited attention so far. This letter bridges these gaps by introducing a new benchmark designed to evaluate VPR performance under the influence of motion blur and image deblurring. The benchmark includes three datasets that encompass a wide range of motion blur intensities, providing a comprehensive platform for analysis. Experimental results with several well-established VPR and image deblurring methods provide new insights into the effects of motion blur and the potential improvements achieved through deblurring. Building on these findings, the letter proposes adaptive deblurring strategies for VPR, designed to effectively manage motion blur in dynamic, real-world scenarios.ensuremath</pensuremath>},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ismagilov, Timur; Ferrarini, Bruno; Milford, Michael; Tuyen, Nguyen Tan Viet; Ramchurn, Sarvapali D.; Ehsan, Shoaib
On motion blur and deblurring in visual place recognition Journal Article
In: IEEE Robotics and Automation Letters, vol. 10, no. 5, pp. 4746–4753, 2025.
Abstract | Links | BibTeX | Tags: data sets for robotic vision, Localization, Vision-Based Navigation
@article{soton507348,
title = {On motion blur and deblurring in visual place recognition},
author = {Timur Ismagilov and Bruno Ferrarini and Michael Milford and Nguyen Tan Viet Tuyen and Sarvapali D. Ramchurn and Shoaib Ehsan},
url = {https://eprints.soton.ac.uk/507348/},
year = {2025},
date = {2025-03-01},
journal = {IEEE Robotics and Automation Letters},
volume = {10},
number = {5},
pages = {4746–4753},
abstract = {ensuremath<pensuremath>Visual Place Recognition (VPR) in mobile robotics enables robots to localize themselves by recognizing previously visited locations using visual data. While the reliability of VPR methods has been extensively studied under conditions such as changes in illumination, season, weather and viewpoint, the impact of motion blur is relatively unexplored despite its relevance not only in rapid motion scenarios but also in low-light conditions where longer exposure times are necessary. Similarly, the role of image deblurring in enhancing VPR performance under motion blur has received limited attention so far. This letter bridges these gaps by introducing a new benchmark designed to evaluate VPR performance under the influence of motion blur and image deblurring. The benchmark includes three datasets that encompass a wide range of motion blur intensities, providing a comprehensive platform for analysis. Experimental results with several well-established VPR and image deblurring methods provide new insights into the effects of motion blur and the potential improvements achieved through deblurring. Building on these findings, the letter proposes adaptive deblurring strategies for VPR, designed to effectively manage motion blur in dynamic, real-world scenarios.ensuremath</pensuremath>},
keywords = {data sets for robotic vision, Localization, Vision-Based Navigation},
pubstate = {published},
tppubtype = {article}
}
Ismagilov, Timur; Ferrarini, Bruno; Milford, Michael; Tuyen, Nguyen Tan Viet; Ramchurn, Sarvapali D.; Ehsan, Shoaib
On motion blur and deblurring in visual place recognition Journal Article
In: IEEE Robotics and Automation Letters, vol. 10, no. 5, pp. 4746–4753, 2025.
@article{soton507348,
title = {On motion blur and deblurring in visual place recognition},
author = {Timur Ismagilov and Bruno Ferrarini and Michael Milford and Nguyen Tan Viet Tuyen and Sarvapali D. Ramchurn and Shoaib Ehsan},
url = {https://eprints.soton.ac.uk/507348/},
year = {2025},
date = {2025-03-01},
journal = {IEEE Robotics and Automation Letters},
volume = {10},
number = {5},
pages = {4746–4753},
abstract = {ensuremath<pensuremath>Visual Place Recognition (VPR) in mobile robotics enables robots to localize themselves by recognizing previously visited locations using visual data. While the reliability of VPR methods has been extensively studied under conditions such as changes in illumination, season, weather and viewpoint, the impact of motion blur is relatively unexplored despite its relevance not only in rapid motion scenarios but also in low-light conditions where longer exposure times are necessary. Similarly, the role of image deblurring in enhancing VPR performance under motion blur has received limited attention so far. This letter bridges these gaps by introducing a new benchmark designed to evaluate VPR performance under the influence of motion blur and image deblurring. The benchmark includes three datasets that encompass a wide range of motion blur intensities, providing a comprehensive platform for analysis. Experimental results with several well-established VPR and image deblurring methods provide new insights into the effects of motion blur and the potential improvements achieved through deblurring. Building on these findings, the letter proposes adaptive deblurring strategies for VPR, designed to effectively manage motion blur in dynamic, real-world scenarios.ensuremath</pensuremath>},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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Ismagilov, Timur; Ferrarini, Bruno; Milford, Michael; Tuyen, Nguyen Tan Viet; Ramchurn, Sarvapali D.; Ehsan, Shoaib
On motion blur and deblurring in visual place recognition Journal Article
In: IEEE Robotics and Automation Letters, vol. 10, no. 5, pp. 4746–4753, 2025.
@article{soton507348,
title = {On motion blur and deblurring in visual place recognition},
author = {Timur Ismagilov and Bruno Ferrarini and Michael Milford and Nguyen Tan Viet Tuyen and Sarvapali D. Ramchurn and Shoaib Ehsan},
url = {https://eprints.soton.ac.uk/507348/},
year = {2025},
date = {2025-03-01},
journal = {IEEE Robotics and Automation Letters},
volume = {10},
number = {5},
pages = {4746–4753},
abstract = {ensuremath<pensuremath>Visual Place Recognition (VPR) in mobile robotics enables robots to localize themselves by recognizing previously visited locations using visual data. While the reliability of VPR methods has been extensively studied under conditions such as changes in illumination, season, weather and viewpoint, the impact of motion blur is relatively unexplored despite its relevance not only in rapid motion scenarios but also in low-light conditions where longer exposure times are necessary. Similarly, the role of image deblurring in enhancing VPR performance under motion blur has received limited attention so far. This letter bridges these gaps by introducing a new benchmark designed to evaluate VPR performance under the influence of motion blur and image deblurring. The benchmark includes three datasets that encompass a wide range of motion blur intensities, providing a comprehensive platform for analysis. Experimental results with several well-established VPR and image deblurring methods provide new insights into the effects of motion blur and the potential improvements achieved through deblurring. Building on these findings, the letter proposes adaptive deblurring strategies for VPR, designed to effectively manage motion blur in dynamic, real-world scenarios.ensuremath</pensuremath>},
keywords = {},
pubstate = {published},
tppubtype = {article}
}