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.
|Number of pages||8|
|Publication status||Published - 2020|
|Event||9th international conference on Pedestrian and Evacuation Dynamics - Lund, Sweden|
Duration: 21 Aug 2018 → 24 Aug 2018