Car detection using cascade classifier on embedded platform
Advanced Driver-Assistance Systems (ADAS) help reducing traffic accidents caused by distracted driving. One of the features of ADAS is Forward Collision Warning System (FCWS). In FCWS, car detection is a crucial step. This paper explains about car detection system using cascade classifier running on...
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Institute of Electrical and Electronics Engineers Inc.
2020
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| Online Access: | http://irep.iium.edu.my/78403/ http://irep.iium.edu.my/78403/ http://irep.iium.edu.my/78403/ http://irep.iium.edu.my/78403/1/78403_Car%20Detection%20Using%20Cascade%20Classifier%20_complete.pdf http://irep.iium.edu.my/78403/7/78403_Car%20Detection%20Using%20Cascade%20Classifier%20_scopus.pdf |
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eprints |
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iium-784032020-03-18T08:02:09Z http://irep.iium.edu.my/78403/ Car detection using cascade classifier on embedded platform Zulkhairi, Muhammad Asyraf Mohd Mustafah, Yasir Zainal Abidin, Zulkifli Mohd Zaki, Hasan Firdaus Abdul Rahman, Hasbullah QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Advanced Driver-Assistance Systems (ADAS) help reducing traffic accidents caused by distracted driving. One of the features of ADAS is Forward Collision Warning System (FCWS). In FCWS, car detection is a crucial step. This paper explains about car detection system using cascade classifier running on embedded platform. The embedded platform used is NXP SBC-S32V234 evaluation board with 64-bit Quad ARM Cortex-A53. The system algorithm is developed in C++ programming language and used open source computer vision library, OpenCV. For car detection process, object detection by cascade classifier method is used. We trained the cascade detector using positive and negative instances mostly from our self-collected Malaysian road dataset. The tested car detection system gives about 88.3 percent detection accuracy with images of 340 by 135 resolution (after cropped and resized). When running on the embedded platform, it managed to get average 13 frames per second with video file input and average 15 frames per second with camera input. Institute of Electrical and Electronics Engineers Inc. 2020-01-09 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/78403/1/78403_Car%20Detection%20Using%20Cascade%20Classifier%20_complete.pdf application/pdf en http://irep.iium.edu.my/78403/7/78403_Car%20Detection%20Using%20Cascade%20Classifier%20_scopus.pdf Zulkhairi, Muhammad Asyraf and Mohd Mustafah, Yasir and Zainal Abidin, Zulkifli and Mohd Zaki, Hasan Firdaus and Abdul Rahman, Hasbullah (2020) Car detection using cascade classifier on embedded platform. In: 7th International Conference on Mechatronics Engineering, ICOM 2019, 30th–31st October 2019, Putrajaya. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8952064 10.1109/ICOM47790.2019.8952064 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English English |
| topic |
QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering |
| spellingShingle |
QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Zulkhairi, Muhammad Asyraf Mohd Mustafah, Yasir Zainal Abidin, Zulkifli Mohd Zaki, Hasan Firdaus Abdul Rahman, Hasbullah Car detection using cascade classifier on embedded platform |
| description |
Advanced Driver-Assistance Systems (ADAS) help reducing traffic accidents caused by distracted driving. One of the features of ADAS is Forward Collision Warning System (FCWS). In FCWS, car detection is a crucial step. This paper explains about car detection system using cascade classifier running on embedded platform. The embedded platform used is NXP SBC-S32V234 evaluation board with 64-bit Quad ARM Cortex-A53. The system algorithm is developed in C++ programming language and used open source computer vision library, OpenCV. For car detection process, object detection by cascade classifier method is used. We trained the cascade detector using positive and negative instances mostly from our self-collected Malaysian road dataset. The tested car detection system gives about 88.3 percent detection accuracy with images of 340 by 135 resolution (after cropped and resized). When running on the embedded platform, it managed to get average 13 frames per second with video file input and average 15 frames per second with camera input. |
| format |
Conference or Workshop Item |
| author |
Zulkhairi, Muhammad Asyraf Mohd Mustafah, Yasir Zainal Abidin, Zulkifli Mohd Zaki, Hasan Firdaus Abdul Rahman, Hasbullah |
| author_facet |
Zulkhairi, Muhammad Asyraf Mohd Mustafah, Yasir Zainal Abidin, Zulkifli Mohd Zaki, Hasan Firdaus Abdul Rahman, Hasbullah |
| author_sort |
Zulkhairi, Muhammad Asyraf |
| title |
Car detection using cascade classifier on embedded platform |
| title_short |
Car detection using cascade classifier on embedded platform |
| title_full |
Car detection using cascade classifier on embedded platform |
| title_fullStr |
Car detection using cascade classifier on embedded platform |
| title_full_unstemmed |
Car detection using cascade classifier on embedded platform |
| title_sort |
car detection using cascade classifier on embedded platform |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2020 |
| url |
http://irep.iium.edu.my/78403/ http://irep.iium.edu.my/78403/ http://irep.iium.edu.my/78403/ http://irep.iium.edu.my/78403/1/78403_Car%20Detection%20Using%20Cascade%20Classifier%20_complete.pdf http://irep.iium.edu.my/78403/7/78403_Car%20Detection%20Using%20Cascade%20Classifier%20_scopus.pdf |
| first_indexed |
2023-09-18T21:50:29Z |
| last_indexed |
2023-09-18T21:50:29Z |
| _version_ |
1777413735950843904 |