End-to-end Vehicle Tracking and Counting in Traffic Videos
thesisposted on 01.08.2019 by Yanzi Jin
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
With the reduced manufacturing cost of cameras and the progress in the computer vision field, intelligent transportation via computer vision has raised much attention. However, there remains a huge gap between academic computer vision research and application. There lacks enough attention to end-to-end computer vision system real-time processing speed. This thesis aims to bridge the gap between the state-of-the-art computer vision research and real-world application. We first address the critical problem of proper initialization and termination in object tracking algorithm and propose a heuristic method for automatic tracking initialization and termination in chapter 2. Then we work on learning the scene-specific semantic knowledge and apply them for other tasks such as vehicle tracking and counting in chapter 3 to 5. Chapter 7 describes our public dataset from real traffic cameras. Chapter 2, 6 and 7 consist of the work before the preliminary exam, which is a complete end-to-end vehicle tracking and counting system running in real time. We demonstrate the performance improvement by the heuristic method and further boost by the semantic knowledge.