The major part of world-wide population moved to urban areas. After such process many issues of major cities have worsened, e.g. air pollution, traffic, security. The increase of security cameras and the improvements of Computer Vision algorithm can be a good solution for many of those problems. The work in this thesis was started after a grant by Park Smart s.r.l., a company located in Catania, which believes that Computer Vision can be the answer for parking space management. The main problem the company has to face is to find a fast way to deploy working solutions, lowering the labeling effort to the minimum, across different scene, cities, parking areas. During the three years of doctoral studies we have tried to solve the problem through the use of various methods such as Semi-Supervised Learning, Counting and Scene Adaptation through Image Classification, Object Detection and Semantic Segmentation. Semi-Supervised classification was the first approach used to decrease labeling effort for fast deployment. Methods based on counting objects, like cars and parking spots, were analyzed as second solution. To gain full knowledge of the scene we focused on Semantic Segmentation and the use of Generative Adversarial Networks in order to find a viable way to reach good Scene Adaptation results comparable to state-of-the-art methods.

For more information, click below for "Scene Understanding for Parking Spaces Management".

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