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.
