Road User Detection and Analysis in Traffic Surveillance Videos
Jinling Li · Summit (Simon Fraser University) · 2014
Road user data collection and behaviour analysis has been an active research topic in the last decade.Automated solutions can be achieved based on video analysis with computer vision techniques.In this thesis, we propose a method to estimate traffic objects' locations with state-of-the-art vision features and learning models.Our focus is put on the applications of cyclist's helmet recognition and 3D vehicle localization.With limited human labelling, we adopt a semi-supervised learning process: tri-training with views of shapes and motion flow for vehicle detection.Experiments are conducted in real-world traffic surveillance videos.First and foremost, I want to give my sincere thanks to my supervisor Dr. Greg Mori for his patience, motivation, enthusiasm, and immense knowledge.His encouragement and guidance support me throughout my master study.He is such a wonderful person that his advice is always designed for maximizing our benefits.One simply could not wish for a better, nicer and wiser supervisor.I would also like