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 misclassifi-cations 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.
CITATION STYLE
Perrett, T., & Mirmehdi, M. (2017). Cost-based feature transfer for vehicle occupant classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10116 LNCS, pp. 405–419). Springer Verlag. https://doi.org/10.1007/978-3-319-54407-6_27
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