Advanced Pedestrian Distance Estimation for ADAS with Canny Edge Detection and Stereo Vision
Pedestrian detection is a vital aspect of Advanced Driver Assistance Systems (ADAS), crucial for ensuring driving safety and minimizing collision risks. While detecting pedestrians is important, it must be paired with precise distance estimation to create a robust safety solution. Stereovision camer...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
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| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00060.pdf |
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| author | Rachidi Oumayma Ed-Dahmani Chafik Bououlid Idrissi Badr |
| author_facet | Rachidi Oumayma Ed-Dahmani Chafik Bououlid Idrissi Badr |
| author_sort | Rachidi Oumayma |
| collection | DOAJ |
| description | Pedestrian detection is a vital aspect of Advanced Driver Assistance Systems (ADAS), crucial for ensuring driving safety and minimizing collision risks. While detecting pedestrians is important, it must be paired with precise distance estimation to create a robust safety solution. Stereovision cameras are well-regarded for their effectiveness and affordability in measuring depth through disparity between two images. Despite this, research on pedestrian distance estimation using only stereovision remains sparse, with many studies relying on computationally heavy dense depth maps. This paper proposes an innovative method for computing object-level disparity specifically for pedestrian detection using stereo cameras. The approach integrates Canny edge detection with ORB (Oriented FAST and Rotated BRIEF) feature matching to efficiently identify and track keypoints within pedestrian bounding boxes. This method not only improves the accuracy of distance estimation but also reduces computational demands, making it suitable for real-time applications. The approach was thoroughly tested on a Raspberry Pi 4, a resource-constrained device, and achieved promising results, demonstrating its potential for practical use in ADAS. |
| format | Article |
| id | doaj-art-78747c8b31ad47f8866cf13236429116 |
| institution | Kabale University |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-78747c8b31ad47f8866cf132364291162025-02-05T10:46:25ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016010006010.1051/e3sconf/202560100060e3sconf_icegc2024_00060Advanced Pedestrian Distance Estimation for ADAS with Canny Edge Detection and Stereo VisionRachidi Oumayma0Ed-Dahmani Chafik1Bououlid Idrissi Badr2Electromechanical Engineering Department, ENSAM of Meknes, Moulay Ismail UniversityElectromechanical Engineering Department, ENSAM of Meknes, Moulay Ismail UniversityElectromechanical Engineering Department, ENSAM of Meknes, Moulay Ismail UniversityPedestrian detection is a vital aspect of Advanced Driver Assistance Systems (ADAS), crucial for ensuring driving safety and minimizing collision risks. While detecting pedestrians is important, it must be paired with precise distance estimation to create a robust safety solution. Stereovision cameras are well-regarded for their effectiveness and affordability in measuring depth through disparity between two images. Despite this, research on pedestrian distance estimation using only stereovision remains sparse, with many studies relying on computationally heavy dense depth maps. This paper proposes an innovative method for computing object-level disparity specifically for pedestrian detection using stereo cameras. The approach integrates Canny edge detection with ORB (Oriented FAST and Rotated BRIEF) feature matching to efficiently identify and track keypoints within pedestrian bounding boxes. This method not only improves the accuracy of distance estimation but also reduces computational demands, making it suitable for real-time applications. The approach was thoroughly tested on a Raspberry Pi 4, a resource-constrained device, and achieved promising results, demonstrating its potential for practical use in ADAS.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00060.pdfstereovisiondistance estimationpedestrian detectioncanny edge detectionorb |
| spellingShingle | Rachidi Oumayma Ed-Dahmani Chafik Bououlid Idrissi Badr Advanced Pedestrian Distance Estimation for ADAS with Canny Edge Detection and Stereo Vision E3S Web of Conferences stereovision distance estimation pedestrian detection canny edge detection orb |
| title | Advanced Pedestrian Distance Estimation for ADAS with Canny Edge Detection and Stereo Vision |
| title_full | Advanced Pedestrian Distance Estimation for ADAS with Canny Edge Detection and Stereo Vision |
| title_fullStr | Advanced Pedestrian Distance Estimation for ADAS with Canny Edge Detection and Stereo Vision |
| title_full_unstemmed | Advanced Pedestrian Distance Estimation for ADAS with Canny Edge Detection and Stereo Vision |
| title_short | Advanced Pedestrian Distance Estimation for ADAS with Canny Edge Detection and Stereo Vision |
| title_sort | advanced pedestrian distance estimation for adas with canny edge detection and stereo vision |
| topic | stereovision distance estimation pedestrian detection canny edge detection orb |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00060.pdf |
| work_keys_str_mv | AT rachidioumayma advancedpedestriandistanceestimationforadaswithcannyedgedetectionandstereovision AT eddahmanichafik advancedpedestriandistanceestimationforadaswithcannyedgedetectionandstereovision AT bououlididrissibadr advancedpedestriandistanceestimationforadaswithcannyedgedetectionandstereovision |