This report is a master thesis in Vision, Graphics, and Interactive Systems. It details the work done during two semesters abroad at UC San Diego. The work has been research oriented, so the report is structured with 5 separate chapters instead of a linear product development flow.

The work has primarily been on US traffic sign detection, but includes a chapter on pedestrian detection as well. A comprehensive survey of traffic sign detection systems has been made and it shows a lack of work with US signs and a lack of public databases for those. Thus, a publicly available dataset with nearly 8000 annotated signs has been created. The dataset is unique, not only because it contains US signs, but also because it include videos. This report also details investigations of using synthetic training data for traffic sign detectors, but concludes that synthetic images are no match for real-world training images. A purely model based detection system based solely on shapes is also presented as a building block for a full detection system. Finally, a two-stage pedestrian detection system has been developed and documented. The system extends a prevous system and produces better detection with fewer false positives.

The work has resulted in the submission of four papers, one to ITS Transactions, one to ICPR and two to ITSC. A the time of writing the journal paper is in the second review stage.