A Robust Anchor-based Method for Multi-Camera Pedestrian Localization

TL;DR

This paper introduces an anchor-based approach for multi-camera pedestrian localization that enhances accuracy and robustness against camera parameter errors, validated through extensive experiments on various datasets.

Contribution

The paper presents a novel anchor-based method that improves pedestrian localization accuracy and robustness to camera calibration errors in multi-camera systems.

Findings

Significant accuracy improvements over existing methods.

Robustness to camera parameter noise demonstrated.

Validated on simulated, real-world, and public datasets.

Abstract

This paper addresses the problem of vision-based pedestrian localization, which estimates a pedestrian's location using images and camera parameters. In practice, however, calibrated camera parameters often deviate from the ground truth, leading to inaccuracies in localization. To address this issue, we propose an anchor-based method that leverages fixed-position anchors to reduce the impact of camera parameter errors. We provide a theoretical analysis that demonstrates the robustness of our approach. Experiments conducted on simulated, real-world, and public datasets show that our method significantly improves localization accuracy and remains resilient to noise in camera parameters, compared to methods without anchors.