Joint probabilistic data fusion for pedestrian detection in multimodal images
- Author
- Zuhaib Ahmed Shaikh (UGent) , David Van Hamme (UGent) , Peter Veelaert (UGent) and Wilfried Philips (UGent)
- Organization
- Abstract
- Pedestrian detection systems are one of the emerging technologies due to their wide range of applications. However, due to the dynamic environments, the data obtained from a single visual sensor for these operations is not sufficient to cover all the environmental conditions, such as varying natural light, weather conditions, etc. Therefore, the use of multiple heterogeneous visual sensors for such systems is indispensable. However, this introduces another challenge of fusing diverse data from multiple visual sensors needing precise geometric alignment. This paper focuses on late fusion based on colour and thermal images using a probabilistic approach conditioned on the environmental variables affecting the sensor data, using off-the-shelf pedestrian detectors. This eliminates the need to train detectors on large annotated dataset and provides the way to deal with the situation when one of the sensor starts degrading or is unavailable. The results in this paper outperform state-of-the-art fusion methods by improving the overall accuracy of the system and providing adaptability when one sensor is unavailable.
- Keywords
- Sensor fusion, Late fusion, Probabilistic fusion, Image color analysis, Data integration, Thermal sensors, Probabilistic logic, Pedestrian Detection
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01HGXB04RG152V4WXYX24G0MTH
- MLA
- Ahmed Shaikh, Zuhaib, et al. “Joint Probabilistic Data Fusion for Pedestrian Detection in Multimodal Images.” 2023 IEEE SENSORS, IEEE, 2023, pp. 1–4, doi:10.1109/SENSORS56945.2023.10325243.
- APA
- Ahmed Shaikh, Z., Van Hamme, D., Veelaert, P., & Philips, W. (2023). Joint probabilistic data fusion for pedestrian detection in multimodal images. 2023 IEEE SENSORS, 1–4. https://doi.org/10.1109/SENSORS56945.2023.10325243
- Chicago author-date
- Ahmed Shaikh, Zuhaib, David Van Hamme, Peter Veelaert, and Wilfried Philips. 2023. “Joint Probabilistic Data Fusion for Pedestrian Detection in Multimodal Images.” In 2023 IEEE SENSORS, 1–4. IEEE. https://doi.org/10.1109/SENSORS56945.2023.10325243.
- Chicago author-date (all authors)
- Ahmed Shaikh, Zuhaib, David Van Hamme, Peter Veelaert, and Wilfried Philips. 2023. “Joint Probabilistic Data Fusion for Pedestrian Detection in Multimodal Images.” In 2023 IEEE SENSORS, 1–4. IEEE. doi:10.1109/SENSORS56945.2023.10325243.
- Vancouver
- 1.Ahmed Shaikh Z, Van Hamme D, Veelaert P, Philips W. Joint probabilistic data fusion for pedestrian detection in multimodal images. In: 2023 IEEE SENSORS. IEEE; 2023. p. 1–4.
- IEEE
- [1]Z. Ahmed Shaikh, D. Van Hamme, P. Veelaert, and W. Philips, “Joint probabilistic data fusion for pedestrian detection in multimodal images,” in 2023 IEEE SENSORS, Vienna, Austria, 2023, pp. 1–4.
@inproceedings{01HGXB04RG152V4WXYX24G0MTH,
abstract = {{Pedestrian detection systems are one of the emerging technologies due to their wide range of applications. However, due to the dynamic environments, the data obtained from a single visual sensor for these operations is not sufficient to cover all the environmental conditions, such as varying natural light, weather conditions, etc. Therefore, the use of multiple heterogeneous visual sensors for such systems is indispensable. However, this introduces another challenge of fusing diverse data from multiple visual sensors needing precise geometric alignment. This paper focuses on late fusion based on colour and thermal images using a probabilistic approach conditioned on the environmental variables affecting the sensor data, using off-the-shelf pedestrian detectors. This eliminates the need to train detectors on large annotated dataset and provides the way to deal with the situation when one of the sensor starts degrading or is unavailable. The results in this paper outperform state-of-the-art fusion methods by improving the overall accuracy of the system and providing adaptability when one sensor is unavailable.}},
author = {{Ahmed Shaikh, Zuhaib and Van Hamme, David and Veelaert, Peter and Philips, Wilfried}},
booktitle = {{2023 IEEE SENSORS}},
isbn = {{9798350303889}},
issn = {{1930-0395}},
keywords = {{Sensor fusion,Late fusion,Probabilistic fusion,Image color analysis,Data integration,Thermal sensors,Probabilistic logic,Pedestrian Detection}},
language = {{eng}},
location = {{Vienna, Austria}},
pages = {{1--4}},
publisher = {{IEEE}},
title = {{Joint probabilistic data fusion for pedestrian detection in multimodal images}},
url = {{http://doi.org/10.1109/SENSORS56945.2023.10325243}},
year = {{2023}},
}
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