Occupant classification for smart airbag using Bayesian filtering

Shih Shinh Huang*, 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

5 引文 斯高帕斯(Scopus)

摘要

Occupant classification is essential for developing a smart airbag system that can intelligently decide to either turn off or deploy according to the type of the occupants. This paper presents a probabilistic approach to recognize the occupant type from a video sequence. Instead of assuming that the frames are mutually independent, we take the relation between two consecutive frames into consideration. Thus, the problem of occupant classification is formulated by introducing the Bayesian filtering which imposes both transition and measurement terms for the inference of the occupant class. For evaluating measurement term, the higher-order Tchebichef moments of edge maps is computed and then an Adaboost learning algorithm is applied to select a set of discriminative moments as the features. For incorporating the temporal coherence, a finite state machine is used to model the transition probabilities among the occupant classes. Finally, the occupant type is estimated by maximizing the posterior probability. Experimental results for several videos with illumination variation are provided to validate the proposed approach.

原文英語
主出版物標題1st International Conference on Green Circuits and Systems, ICGCS 2010
頁面660-665
頁數6
DOIs
出版狀態已出版 - 2010
對外發佈
事件1st International Conference on Green Circuits and Systems, ICGCS 2010 - Shanghai, 中國
持續時間: 21 06 201023 06 2010

出版系列

名字1st International Conference on Green Circuits and Systems, ICGCS 2010

Conference

Conference1st International Conference on Green Circuits and Systems, ICGCS 2010
國家/地區中國
城市Shanghai
期間21/06/1023/06/10

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