  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.
