Accurate pedestrian localization in overhead depth images via Height-Augmented HOG

Werner Kroneman, Alessandro Corbetta, Federico Toschi


We tackle the challenge of reliably and automatically localizing pedestrians in real-life conditions through overhead depth imaging at unprecedented high-density conditions. Leveraging upon a combination of Histogram of Oriented Gradients-like feature descriptors, neural networks, data augmentation and custom data annotation strategies, this work contributes a robust and scalable machine learning-based localization algorithm, which delivers near-human localization performance in real-time, even with local pedestrian density of about 3 ped/m2, a case in which most stateof- the art algorithms degrade significantly in performance.


real-life pedestrian dynamics measurements; depth-based localization; machine learning; augmented HOG features; high-density localization

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Copyright (c) 2020 Werner Kroneman, Alessandro Corbetta, Federico Toschi

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