Driver distraction is a significant factor in the occurence of car crashes. Its detection is a topic of interest in the automotive industry. The purpose of driver distraction detection is to reduce risk of crashes by giving advanced warning of driver inattentiveness, enabling warning signals or automotive system actions.  Using in-vehicle cameras, the body pose of a driver can be determined. With that an estimation of attentiveness could be made by a software system.

In this thesis, a concept for a body pose estimation approach using a depth camera is described. Sensor choice and the subsequent development of a frame acquisition pipeline is laid out. A test video generation plan is introduced and its merits discussed. A static background filter algorithm is designed and implemented. Estimation of arm positions in point cloud data is explored using the sample consensus functionality provided by the Point Cloud Library. The methods for generating and finding parameters used for arm position estimation are presented. A measurement for the performance is presented and intermediate results are evaluated. A further iteration on paramteter generation is shown to improve the performance of the approach by narrowing down parameters. The final results are presented and factors leading to differences in the performance are discussed.

The thesis is concluded by a summary of the results and in an outlook further developments of the system are presented and discussed.