Detecting unsafe driving patterns using discriminative learning

Zhou Yue, Xu Wei, Ning Huazhong, Gong Yihong, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a discriminative learning approach for fusing multichannel sequential data with application to detect unsafe driving patterns from multi-channel driving recording data. The fusion is performed using a discriminatively trained graphical model - conditional random field (CRF). The proposed approach offers several advantage over existing information fusing approaches. First, it derives its classification power by directly modelling and maximizing the conditional probability. Second, it represents the variable dependency in an undirected graph, which is very efficient in inference. Third, it does not require to label all the training data and utilizes both labelled and unlabelled data efficiently by semi-supervised learning algorithms. The proposed approach is evaluated on driving recording data collected from driving simulator - STISIM. Experiments show it outperforms the simple discriminative classifier (SVM) and generative model (HMM).

Original languageEnglish (US)
Title of host publicationProceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007
Pages1431-1434
Number of pages4
StatePublished - 2007
EventIEEE International Conference onMultimedia and Expo, ICME 2007 - Beijing, China
Duration: Jul 2 2007Jul 5 2007

Publication series

NameProceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007

Other

OtherIEEE International Conference onMultimedia and Expo, ICME 2007
CountryChina
CityBeijing
Period7/2/077/5/07

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Software

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