Møgelmose, Andreas

English

Automated analysis of traffic situations - be it cars, signs, or pedestrians -is becoming increasingly relevant and feasible with the advent of powerfulsensors, computers, and algorithms. This PhD thesis tackles three themeswithin 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 stateof 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 thatdetection of U.S. traffic signs has traditionally lacked behind detection ofEuropean signs, which led to the effort of collecting the dataset and pushingthe state of the art in detection performance for these signs by using theAggregate Channel Features detector.Within pedestrian detection, a method combining Viola-Jones and HOG/SVMhas been put forth, which gives the speed advantage of Viola-Jones and thedetection performance of HOG/SVM. Work has also been done in trackingthe gaze of drivers to determine which pedestrians a driver may have missed.Finally, pedestrian tracking has been performed in an attempt to predict theirfuture 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 fromregular surveillance cameras, but we added depth and thermal informationto the mix. Several iterations of a multi-modal system were tested, but theadvantage of the additional information turned out to be limited.<br/

Visual Analysis in Traffic &amp; Re-identification

https://vbn.aau.dk/ws/files/316439577/PHD_Andrea_M_gelmose_E_pdf.pdf

Visual Analysis in Traffic & Re-identification

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