A Robust Anchor-based Method for Multi-Camera Pedestrian Localization
This addresses localization errors for vision-based systems in practical settings, but it is incremental as it builds on existing anchor-based approaches.
The paper tackles pedestrian localization with inaccurate camera parameters by proposing an anchor-based method, which significantly improves accuracy and robustness in experiments on simulated, real-world, and public datasets.
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.