3D Modelling for Improved Visual Traffic Analytics

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dc.contributor.advisorElder, James H.
dc.creatorSoto, Eduardo R Corral
dc.date.accessioned2018-08-27T16:38:16Z
dc.date.available2018-08-27T16:38:16Z
dc.date.copyright2018-03-15
dc.date.issued2018-08-27
dc.date.updated2018-08-27T16:38:16Z
dc.degree.disciplineComputer Science and Engineering
dc.degree.levelDoctoral
dc.degree.namePhD - Doctor of Philosophy
dc.description.abstractAdvanced Traffic Management Systems utilize diverse types of sensor networks with the goal of improving mobility and safety of transportation systems. These systems require information about the state of the traffic configuration, including volume, vehicle speed, density, and incidents, which are useful in applications such as urban planning, collision avoidance systems, and emergency vehicle notification systems, to name a few. Sensing technologies are an important part of Advanced Traffic Management Systems that enable the estimation of the traffic state. Inductive Loop Detectors are often used to sense vehicles on highway roads. Although this technology has proven to be effective, it has limitations. Their installation and replacement cost is high and causes traffic disruptions, and their sensing modality provides very limited information about the vehicles being sensed. No vehicle appearance information is available. Traffic camera networks are also used in advanced traffic monitoring centers where the cameras are controlled by a remote operator. The amount of visual information provided by such cameras can be overwhelmingly large, which may cause the operators to miss important traffic events happening in the field. This dissertation focuses on visual traffic surveillance for Advanced Traffic Management Systems. The focus is on the research and development of computer vision algorithms that contribute to the automation of highway traffic analytics systems that require estimates of traffic volume and density. This dissertation makes three contributions: The first contribution is an integrated vision surveillance system called 3DTown, where cameras installed at a university campus together with algorithms are used to produce vehicle and pedestrian detections to augment a 3D model of the university with dynamic information from the scene. A second major contribution is a technique for extracting road lines from highway images that are used to estimate the tilt angle and the focal length of the camera. This technique is useful when the operator changes the camera pose. The third major contribution is a method to automatically extract the active road lanes and model the vehicles in 3D to improve the vehicle count estimation by individuating 2D segments of imaged vehicles that have been merged due to occlusions.
dc.identifier.urihttp://hdl.handle.net/10315/34994
dc.language.isoen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectApplied mathematics
dc.subject.keywordsComputer vision
dc.subject.keywordsAlgorithms
dc.subject.keywordsMachine learning
dc.subject.keywordsTraffic surveillance
dc.subject.keywordsCamera calibration
dc.subject.keywords3D object detection
dc.subject.keywords3D modelling
dc.subject.keywordsVirtual reality
dc.subject.keywordsRoad markings
dc.subject.keywordsCurved roads
dc.subject.keywordsmcmc
dc.subject.keywordsOptimization
dc.title3D Modelling for Improved Visual Traffic Analytics
dc.typeElectronic Thesis or Dissertation

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