Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Ju, Ting-Fung | en_US |
| dc.contributor.author | Lu, Wei-Min | en_US |
| dc.contributor.author | Chen, Kuan-Hung | en_US |
| dc.contributor.author | Guo, Jiun-In | en_US |
| dc.date.accessioned | 2015-12-02T03:00:58Z | - |
| dc.date.available | 2015-12-02T03:00:58Z | - |
| dc.date.issued | 2014-01-01 | en_US |
| dc.identifier.isbn | 978-1-4799-4851-2 | en_US |
| dc.identifier.issn | en_US | |
| dc.identifier.uri | http://hdl.handle.net/11536/128617 | - |
| dc.description.abstract | This paper presents a vision-based moving objects detection work which attracts much attention in intelligent automobile applications recently. Vision-based objects detection provides object behavior information of objects and is an intuitive detection method similar to human visual perception. Besides, vision-based objects detection methods are much low-cost compared with detection methods such as RADAR (Radio Detection And Ranging), or LiDAR (Light Detection And Ranging). However, current vision-based objects detection methods still suffer from several challenges such as high false alarms and unstable detection rate which limit their value in practical applications. Accordingly, this paper presents a robustness enhancing method for vision-based moving objects detection. | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | intelligent automobile | en_US |
| dc.subject | intelligent vision | en_US |
| dc.subject | object detection | en_US |
| dc.subject | pedestrian detection | en_US |
| dc.title | Vision-Based Moving Objects Detection for Intelligent Automobiles and a Robustness Enhancing Method | en_US |
| dc.type | Proceedings Paper | en_US |
| dc.identifier.journal | 2014 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - TAIWAN (ICCE-TW) | en_US |
| dc.contributor.department | 電子工程學系及電子研究所 | zh_TW |
| dc.contributor.department | Department of Electronics Engineering and Institute of Electronics | en_US |
| dc.identifier.wosnumber | WOS:000361019800034 | en_US |
| dc.citation.woscount | 0 | en_US |
| Appears in Collections: | Conferences Paper | |

