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

arXiv: https://arxiv.org/abs/1805.12510 · HTML: https://mlsanity.com/paper/1805.12510/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2018.

## Built on by (most cited first, 1 of 1)

- [Benchmarking high-fidelity pedestrian tracking systems for research, real-time monitoring and crowd control](https://mlsanity.com/paper/2108.11719/) (2021)

## Abstract (arXiv)

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.