Cost-based Feature Transfer for Vehicle Occupant Classification
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.