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

Werner Kroneman (Corresponding author), Alessandro Corbetta (Corresponding author), Federico Toschi (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review


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.
Original languageEnglish
Pages (from-to)33-40
Number of pages8
JournalCollective Dynamics
Publication statusPublished - 2020
Event9th international conference on Pedestrian and Evacuation Dynamics - Lund, Sweden
Duration: 21 Aug 201824 Aug 2018


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