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• W2971927438 abstract "In 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." @default.
• W2971927438 created "2019-09-12" @default.
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• W2971927438 date "2019-01-01" @default.
• W2971927438 modified "2023-10-01" @default.
• W2971927438 title "Learning Pedestrian Detection from Virtual Worlds" @default.
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• W2971927438 doi "https://doi.org/10.1007/978-3-030-30642-7_27" @default.
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