Range estimation in vision-based forward collision warning system
Also available in printed version
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| Format: | Master's thesis |
| Language: | English |
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Universiti Teknologi Malaysia
2025
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| Online Access: | https://utmik.utm.my/handle/123456789/115938 |
| Abstract | Abstract here |
| _version_ | 1854934138247708672 |
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| author | Mursidi Unir@Hashim |
| author2 | Kamaludin Mohamad Yusof, supervisor |
| author_facet | Kamaludin Mohamad Yusof, supervisor Mursidi Unir@Hashim |
| author_sort | Mursidi Unir@Hashim |
| description | Also available in printed version |
| format | Master's thesis |
| id | utm-123456789-115938 |
| institution | Universiti Teknologi Malaysia |
| language | English |
| publishDate | 2025 |
| publisher | Universiti Teknologi Malaysia |
| record_format | dspace |
| record_pdf | Abstract |
| spelling | utm-123456789-1159382025-08-21T07:36:59Z Range estimation in vision-based forward collision warning system Mursidi Unir@Hashim Kamaludin Mohamad Yusof, supervisor Electrical engineering Also available in printed version Obstacle detection and range estimation are important issues for driver assistance systems. Its have to be performed with high reliability to avoid potential collisions. In most research done to date, the vision-based obstacle detection techniques are highly regarded for this purpose as they require less infrastructure and more economic. However, the performance and robustness of such systems remain challenging to researcher. This research develops and investigates a forward collision warning system using stereo vision sensors (two cameras) which detects vehicles ahead. A depth map is generated from rectified stereo images which contains disparity information which is proportional to the distance of the corresponding world point from the camera. Histograms of oriented gradient (HOG) feature matching is employed to detect the target object. The object distance then can be computed from its 3-D world coordinates. An error compensation model was developed to further improve the accuracy of the distance estimation. The results show that percentage estimation error was reduced after the distance calculation was refined using the proposed error compensation model. The system achieves an average error that is less than 2% at distances up to 15 meters fahmimoksen UTM 87 p. Thesis (Sarjana Kejuruteraan (Elektronik dan Komunikasi)) - Universiti Teknologi Malaysia, 2017 2025-04-22T01:38:58Z 2025-04-22T01:38:58Z 2017 Master's thesis https://utmik.utm.my/handle/123456789/115938 vital:113077 valet-20180718-114541 ENG Closed Access UTM Complete Unpublished Completion application/pdf Universiti Teknologi Malaysia |
| spellingShingle | Electrical engineering Mursidi Unir@Hashim Range estimation in vision-based forward collision warning system |
| thesis_level |
Master
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| title | Range estimation in vision-based forward collision warning system |
| title_full | Range estimation in vision-based forward collision warning system |
| title_fullStr | Range estimation in vision-based forward collision warning system |
| title_full_unstemmed | Range estimation in vision-based forward collision warning system |
| title_short | Range estimation in vision-based forward collision warning system |
| title_sort | range estimation in vision based forward collision warning system |
| topic | Electrical engineering |
| url | https://utmik.utm.my/handle/123456789/115938 |
| work_keys_str_mv | AT mursidiunirhashim rangeestimationinvisionbasedforwardcollisionwarningsystem |