Archives and Documentation Center
Digital Archives

Person detection and tracking using omnidirectional cameras, and rectangle blanket problem

Show simple item record

dc.contributor Ph.D. Program in Computer Engineering.
dc.contributor.advisor Akarun, Lale.
dc.contributor.advisor Salah, Albert Ali.
dc.contributor.author Demiröz, Barış Evrim.
dc.date.accessioned 2023-03-16T10:14:04Z
dc.date.available 2023-03-16T10:14:04Z
dc.date.issued 2019.
dc.identifier.other CMPE 2019 D46 PhD
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/12637
dc.description.abstract Person detection and tracking can provide the crucial analysis needed to avoid accidents with autonomous machinery, optimize environments for effciency and assist the elderly. Omnidirectional cameras have a large field of view that allow them to cover more ground at the expense of resolution. Omnidirectional cameras can decrease setup, maintenance and computational costs by reducing the number of cameras and the bandwidth required. Computer vision methods developed for conventional cameras usually fail for omnidirectional cameras due to their di erent image formation geometry. In this thesis, rst, a novel dataset for person tracking in omnidirectional cameras is introduced. The dataset, namely BOMNI, contains 46 videos of persons moving inside a room; where the bounding boxes and the identity of the persons are annotated at every frame. Second, a generative Bayesian framework is developed for coupling person tracking and fall detection. The method is evaluated on BOMNI dataset, producing 93% tracking accuracy and fall detection within a few frames of the event. Third, a similar method for multiple person tracking is developed and evaluated on BOMNI dataset. The method reaches 86% tracking accuracy, increasing a previous approach by 18%. Fourth, a discriminative method for person detection is presented. Also a novel structure called Radial Integral Image that speeds up feature extraction step is introduced. This method achieves state of the art detection performance on IYTE dataset: 4.5% miss rate for one false positive per image. Finally, the problem of representing a shape with multiple rectangles, Rectangle Blanket Problem, is formulated as an integer programming problem and a branch-and-bound scheme is presented along with a novel branching rule to solve it optimally. This problem is encountered in the earlier sections of this thesis, but it is a general problem that is present in the literature.
dc.format.extent 30 cm.
dc.publisher Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2019.
dc.subject.lcsh Autonomous robots.
dc.subject.lcsh Cameras -- Calibration.
dc.subject.lcsh Three-dimensional imaging.
dc.title Person detection and tracking using omnidirectional cameras, and rectangle blanket problem
dc.format.pages xvii, 119 leaves ;


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search Digital Archive


Browse

My Account