Accurate pedestrian localization in overhead depth images via Height-Augmented HOG
This addresses the problem of reliable pedestrian detection in crowded environments for applications like surveillance or robotics, though it appears incremental as it builds on existing methods like HOG and neural networks.
The paper tackled pedestrian localization in high-density overhead depth images, achieving near-human performance in real-time at densities of about 3 ped/m² where other algorithms degrade.
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 state-of-the art algorithms degrade significantly in performance.