Visual Analysis in Traffic & Re-identification

Automated analysis of traffic situations - be it cars, signs, or pedestrians -
is becoming increasingly relevant and feasible with the advent of powerful
sensors, computers, and algorithms. This PhD thesis tackles three themes
within this realm: Traffic sign detection, pedestrian detection and analysis,
and person re-identification.

In traffic sign detection, the work comprises a thorough survey of the state
of the art, assembly of the worlds largest public dataset with U.S. traffic signs,
and work in machine learning based detection algorithms. It was shown that
detection of U.S. traffic signs has traditionally lacked behind detection of
European signs, which led to the effort of collecting the dataset and pushing
the state of the art in detection performance for these signs by using the
Aggregate Channel Features detector.

Within pedestrian detection, a method combining Viola-Jones and HOG/SVM
has been put forth, which gives the speed advantage of Viola-Jones and the
detection performance of HOG/SVM. Work has also been done in tracking
the gaze of drivers to determine which pedestrians a driver may have missed.
Finally, pedestrian tracking has been performed in an attempt to predict their
future behavior in order to avoid dangerous situations.

Person re-identification has been attempted in a multi-modal fashion. Traditionally,
re-identification has been performed using only RGB input from
regular surveillance cameras, but we added depth and thermal information
to the mix. Several iterations of a multi-modal system were tested, but the
advantage of the additional information turned out to be limited.