| Summary: | The aim of this thesis is to examine the applicability of computer vision to analyze pedestrian and crowd characteristics, and how pedestrain simulation for shopping environments can be driven from the visual perception of the simulated pedestrians. More specifically, two frameworks for pedestrian speed profile estimation are designed and implemented. The first address the problem of speed estimation for people moving parallel to the image plane on a flat surface, while the other tries to estimate the speed of people walking on stairs moving while their trajectories and being perpendicular on the image plane. Both approaches aim to localise the foot of the pedestrains, and by identifying their steps measure their speed. Except from measuring the speed of pedestrains, a crowd counting system using Convolutional Neural Networks is created by exploiting the background spatial persistence of a whole image in the temporal domain, and furthermore by fusing consecutive temporal counting information in the systme further refines its estimates. Finally a novel memory-free cognitive framework for pedestrian shopping behaviour is presented where the simulated pedestrians use as route choice model their visual perception. Agents moving in an environment and equipped with an activity agenda. use their vision to select not only their root choices but also the shops that they visit.
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