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Tecniche di Visione Artificiale per l'Interazione Uomo-Veicolo

Abstract: In recent years, the widespread adoption of digital devices in all aspects of everyday life has led to new research opportunities in the field of Human-Computer Interaction. In the automotive field, where infotainment systems are becoming more and more important to the final user, the availability of inexpensive miniaturized cameras has enabled the development of vision-based Natural User Interfaces, paving the way for novel approaches to the Human-Vehicle Interaction. In this thesis, we investigate computer vision techniques, based on both visible light and non-visible spectrum, that can form the foundation of the next generation of in-vehicle infotainment systems. As sensing technology, we focus on infrared-based devices, such as depth and thermal cameras. They provide reliable data under different illumination conditions, making them a good fit for the mutable automotive environment. Using these acquisition devices, we collect two novel datasets: a facial dataset, to investigate the impact of sensor resolution and quality in changing acquisition settings, and a dataset of dynamic hand gestures, collected with several synchronized sensors within a car simulator. As vision approaches, we adopt state-of-the-art deep learning techniques, focusing on efficient neural networks that can be easily deployed on computing devices on the edge. In this context, we study several computer vision tasks to cover the majority of human-car interactions. First, we investigate the usage of depth cameras for the face recognition task, focusing on how depth-map representations and deep neural models affect the recognition performance. Secondly, we address the problem of in-car dynamic hand gesture recognition in real-time, using depth and infrared sensors. Then, we focus on the analysis of the human body, both in terms of the 3D human pose estimation and the contact-free estimation of anthropometric measurements. Finally, focusing on the area surrounding the vehicle, we explore the 3D reconstruction of objects from 2D images, as a first step towards the 3D visualization of the external environment from controllable viewpoints.


Pini, Stefano "Tecniche di Visione Artificiale per l'Interazione Uomo-Veicolo" 2022

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