Cost-Based Feature Transfer for Vehicle Occupant Classification

  • Toby PerrettEmail author
  • Majid Mirmehdi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10116)


Knowledge of human presence and interaction in a vehicle is of growing interest to vehicle manufacturers for design and safety purposes. We present a framework to perform the tasks of occupant detection and occupant classification for automatic child locks and airbag suppression. It operates for all passenger seats using a single overhead camera. A transfer learning technique is introduced to make full use of training data from all seats, whilst still maintaining some control over the bias necessary for a system designed to penalize certain misclassifications more than others. An evaluation is performed on a challenging dataset with both weighted and unweighted classifiers that demonstrates the effectiveness of the transfer process.


Occupant Classification Transfer Learning Misclassification Cost Average Classification Accuracy Passenger Seat 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Visual Information LaboratoryUniversity of BristolBristolUK

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