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, demonstrating the effectiveness of the transfer process.
@article{arxiv.1512.07080,
title = {Cost-based Feature Transfer for Vehicle Occupant Classification},
author = {Toby Perrett and Majid Mirmehdi and Eduardo Dias},
journal= {arXiv preprint arXiv:1512.07080},
year = {2015}
}