Probabilistic Fusion for Pedestrian Detection from Thermal and Colour Images
Pedestrian detection is an important research domain due to its relevance for autonomous and assisted driving, as well as its applications in security and industrial automation. Often, more than one type of sensor is used to cover a broader range of operating conditions than a single-sensor system w...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/22/8637 |
| _version_ | 1797464018424692736 |
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| author | Zuhaib Ahmed Shaikh David Van Hamme Peter Veelaert Wilfried Philips |
| author_facet | Zuhaib Ahmed Shaikh David Van Hamme Peter Veelaert Wilfried Philips |
| author_sort | Zuhaib Ahmed Shaikh |
| collection | DOAJ |
| description | Pedestrian detection is an important research domain due to its relevance for autonomous and assisted driving, as well as its applications in security and industrial automation. Often, more than one type of sensor is used to cover a broader range of operating conditions than a single-sensor system would allow. However, it remains difficult to make pedestrian detection systems perform well in highly dynamic environments, often requiring extensive retraining of the algorithms for specific conditions to reach satisfactory accuracy, which, in turn, requires large, annotated datasets captured in these conditions. In this paper, we propose a probabilistic decision-level sensor fusion method based on naive Bayes to improve the efficiency of the system by combining the output of available pedestrian detectors for colour and thermal images without retraining. The results in this paper, obtained through long-term experiments, demonstrate the efficacy of our technique, its ability to work with non-registered images, and its adaptability to cope with situations when one of the sensors fails. The results also show that our proposed technique improves the overall accuracy of the system and could be very useful in several applications. |
| first_indexed | 2024-03-09T18:00:59Z |
| format | Article |
| id | doaj.art-661df15a5685445d9b3d689a53502835 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T18:00:59Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-661df15a5685445d9b3d689a535028352023-11-24T09:53:26ZengMDPI AGSensors1424-82202022-11-012222863710.3390/s22228637Probabilistic Fusion for Pedestrian Detection from Thermal and Colour ImagesZuhaib Ahmed Shaikh0David Van Hamme1Peter Veelaert2Wilfried Philips3TELIN-IPI, Ghent University–imec, 9000 Ghent, BelgiumTELIN-IPI, Ghent University–imec, 9000 Ghent, BelgiumTELIN-IPI, Ghent University–imec, 9000 Ghent, BelgiumTELIN-IPI, Ghent University–imec, 9000 Ghent, BelgiumPedestrian detection is an important research domain due to its relevance for autonomous and assisted driving, as well as its applications in security and industrial automation. Often, more than one type of sensor is used to cover a broader range of operating conditions than a single-sensor system would allow. However, it remains difficult to make pedestrian detection systems perform well in highly dynamic environments, often requiring extensive retraining of the algorithms for specific conditions to reach satisfactory accuracy, which, in turn, requires large, annotated datasets captured in these conditions. In this paper, we propose a probabilistic decision-level sensor fusion method based on naive Bayes to improve the efficiency of the system by combining the output of available pedestrian detectors for colour and thermal images without retraining. The results in this paper, obtained through long-term experiments, demonstrate the efficacy of our technique, its ability to work with non-registered images, and its adaptability to cope with situations when one of the sensors fails. The results also show that our proposed technique improves the overall accuracy of the system and could be very useful in several applications.https://www.mdpi.com/1424-8220/22/22/8637sensor fusionprobabilistic fusionnaive Bayesdecision-level fusion |
| spellingShingle | Zuhaib Ahmed Shaikh David Van Hamme Peter Veelaert Wilfried Philips Probabilistic Fusion for Pedestrian Detection from Thermal and Colour Images Sensors sensor fusion probabilistic fusion naive Bayes decision-level fusion |
| title | Probabilistic Fusion for Pedestrian Detection from Thermal and Colour Images |
| title_full | Probabilistic Fusion for Pedestrian Detection from Thermal and Colour Images |
| title_fullStr | Probabilistic Fusion for Pedestrian Detection from Thermal and Colour Images |
| title_full_unstemmed | Probabilistic Fusion for Pedestrian Detection from Thermal and Colour Images |
| title_short | Probabilistic Fusion for Pedestrian Detection from Thermal and Colour Images |
| title_sort | probabilistic fusion for pedestrian detection from thermal and colour images |
| topic | sensor fusion probabilistic fusion naive Bayes decision-level fusion |
| url | https://www.mdpi.com/1424-8220/22/22/8637 |
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