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EnglishIn this paper, we present a real-time pedestrian detection system that has been trained using a virtual environment. This is a very popular topic of research having endless practical applications and recently, there was an increasing interest in deep learning architectures for performing such a task. However, the availability of large labeled datasets is a key point for an effective train of such algorithms. For this reason, in this work, we introduced ViPeD, a new synthetically generated set of images extracted from a realistic 3D video game where the labels can be automatically generated exploiting 2D pedestrian positions extracted from the graphics engine. We exploited this new synthetic dataset fine-tuning a state-of-the-art computationally efficient Convolutional Neural Network (CNN). A preliminary experimental evaluation, compared to the performance of other existing approaches trained on real-world images, shows encouraging results.
| Titolo: | Learning pedestrian detection from virtual worlds | |
| Autori interni: | CIAMPI, LUCA (Co-primo) MESSINA, NICOLA (Co-primo) | |
| Anno del prodotto: | 2019 | |
| Serie: | ||
| Handle: | http://hdl.handle.net/11568/1142542 | |
| ISBN: | 978-3-030-30641-0 978-3-030-30642-7 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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