Occupant classification invariant to seat movement for smart airbag

Shih Shinh Huang*, Er Liang Jian, Pei Yung Hsiao

*此作品的通信作者

研究成果: 圖書/報告稿件的類型會議稿件同行評審

2 引文 斯高帕斯(Scopus)

摘要

This paper presents an occupant classification approach based on monocular vision for smart airbags that can decide to deploy or turn off intelligently. The main focus of this work different from those in the literature is on addressing the issue of the movement of car seat. The idea behind is to introduce the relation between the object of interest and scene inside the vehicle, namely, contextual information, for priming the seat configuration. As for circumventing the problem of lighting change as well as intra-class variance, we model each class by a set of representative parts called patches and describe the patch by using appearance difference rather than appearance itself in the tradition approaches. The selection of patches and the estimation of their parameters are achieved through a boosting algorithm by minimizing the loss of training error instead of using maximum likelihood (ML) strategy. Finally, we evaluate our proposed approach using a great amount of database collected from the camera deployed on a moving platform.

原文英語
主出版物標題Proceedings of 2011 IEEE International Conference on Vehicular Electronics and Safety, ICVES 2011
頁面144-149
頁數6
DOIs
出版狀態已出版 - 2011
對外發佈
事件2011 IEEE International Conference on Vehicular Electronics and Safety, ICVES 2011 - Beijing, 中國
持續時間: 10 07 201112 07 2011

出版系列

名字Proceedings of 2011 IEEE International Conference on Vehicular Electronics and Safety, ICVES 2011

Conference

Conference2011 IEEE International Conference on Vehicular Electronics and Safety, ICVES 2011
國家/地區中國
城市Beijing
期間10/07/1112/07/11

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