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

TL;DR

This paper presents a robust, real-time pedestrian localization method in high-density overhead depth images using a novel Height-Augmented HOG feature descriptor combined with neural networks, outperforming existing algorithms in challenging conditions.

Contribution

The work introduces a new Height-Augmented HOG descriptor and a scalable neural network approach for accurate pedestrian localization in dense overhead depth imagery.

Findings

Achieves near-human localization accuracy in real-time

Maintains high performance at densities of about 3 ped/m2

Outperforms state-of-the-art algorithms in dense scenarios

Abstract

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.