# Large-Scale Person Detection and Localization using Overhead Fisheye Cameras

arXiv: https://arxiv.org/abs/2307.08252 · HTML: https://mlsanity.com/paper/2307.08252/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 80 references that are in the map.
- First posted on arXiv 17 Jul 2023.

## Builds on (most cited first, 29 of 29)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Group Equivariant Convolutional Networks](https://mlsanity.com/paper/1602.07576/) (2016)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://mlsanity.com/paper/2201.12329/) (2022)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018)
- [DSAC - Differentiable RANSAC for Camera Localization](https://mlsanity.com/paper/1611.05705/) (2016)
- [Learning SO(3) Equivariant Representations with Spherical CNNs](https://mlsanity.com/paper/1711.06721/) (2017)
- [Understanding image representations by measuring their equivariance and equivalence](https://mlsanity.com/paper/1411.5908/) (2014)
- [Geometric Loss Functions for Camera Pose Regression with Deep Learning](https://mlsanity.com/paper/1704.00390/) (2017)
- [Back to the Feature: Learning Robust Camera Localization from Pixels to Pose](https://mlsanity.com/paper/2103.09213/) (2021)
- [Image-based localization using LSTMs for structured feature correlation](https://mlsanity.com/paper/1611.07890/) (2016)
- [PlaNet - Photo Geolocation with Convolutional Neural Networks](https://mlsanity.com/paper/1602.05314/) (2016)
- [Expert Sample Consensus Applied to Camera Re-Localization](https://mlsanity.com/paper/1908.02484/) (2019)
- [Generalized Object Detection on Fisheye Cameras for Autonomous Driving: Dataset, Representations and Baseline](https://mlsanity.com/paper/2012.02124/) (2020)
- [From Handcrafted to Deep Features for Pedestrian Detection: A Survey](https://mlsanity.com/paper/2010.00456/) (2020)
- [Robot Localization in Floor Plans Using a Room Layout Edge Extraction Network](https://mlsanity.com/paper/1903.01804/) (2019)
- [Deep ChArUco: Dark ChArUco Marker Pose Estimation](https://mlsanity.com/paper/1812.03247/) (2018)
- [RAPiD: Rotation-Aware People Detection in Overhead Fisheye Images](https://mlsanity.com/paper/2005.11623/) (2020)
- [Neural Reprojection Error: Merging Feature Learning and Camera Pose Estimation](https://mlsanity.com/paper/2103.07153/) (2021)
- [Learning Equivariant Segmentation with Instance-Unique Querying](https://mlsanity.com/paper/2210.00911/) (2022)
- [SiamMask: A Framework for Fast Online Object Tracking and Segmentation](https://mlsanity.com/paper/2207.02088/) (2022)
- [ChromaTag: A Colored Marker and Fast Detection Algorithm](https://mlsanity.com/paper/1708.02982/) (2017)
- [Efficient Pedestrian Detection in Top-View Fisheye Images Using Compositions of Perspective View Patches](https://mlsanity.com/paper/2009.02711/) (2020)
- [Improved Person Detection on Omnidirectional Images with Non-maxima Suppression](https://mlsanity.com/paper/1805.08503/) (2018)
- [Fast Visual Object Tracking with Rotated Bounding Boxes](https://mlsanity.com/paper/1907.03892/) (2019)
- [Resolving Marker Pose Ambiguity by Robust Rotation Averaging with Clique Constraints](https://mlsanity.com/paper/1909.11888/) (2019)

## Built on by (most cited first, 12 of 12)

- [Depth Any Camera: Zero-Shot Metric Depth Estimation from Any Camera](https://mlsanity.com/paper/2501.02464/) (2025)
- [Nonverbal Interaction Detection](https://mlsanity.com/paper/2407.08133/) (2024)
- [RICO: Two Realistic Benchmarks and an In-Depth Analysis for Incremental Learning in Object Detection](https://mlsanity.com/paper/2508.13878/) (2025)
- [Incremental Object Detection with Prompt-based Methods](https://mlsanity.com/paper/2508.14599/) (2025)
- [MULTI: Disentangling Camera Lens, Sensor, View, and Domain for Novel Image Generation](https://mlsanity.com/paper/2605.12134/) (2026)
- [Mean of Means: A 10-dollar Solution for Human Localization with Calibration-free and Unconstrained Camera Settings](https://mlsanity.com/paper/2407.20870/) (2024)
- [Mean of Means: Human Localization with Calibration-free and Unconstrained Camera Settings (extended version)](https://mlsanity.com/paper/2502.13017/) (2025)
- [Panoramic Distortion-Aware Tokenization for Person Detection and Localization in Overhead Fisheye Images](https://mlsanity.com/paper/2503.14228/) (2025)
- [Finding the Reflection Point: Unpadding Images to Remove Data Augmentation Artifacts in Large Open Source Image Datasets for Machine Learning](https://mlsanity.com/paper/2504.03168/) (2025)
- [KAN We Flow? Advancing Robotic Manipulation with 3D Flow Matching via KAN & RWKV](https://mlsanity.com/paper/2602.01115/) (2026)
- [PMOF: A Dataset and Benchmark for Passenger Monitoring Using Overhead Fisheye Cameras](https://mlsanity.com/paper/2606.13910/) (2026)
- [PointRL: Learning Point-Level Vision-Language Grounding from Verifiable Annotation Evidence](https://mlsanity.com/paper/2608.25299/) (2026)

## Abstract (arXiv)

Location determination finds wide applications in daily life. Instead of existing efforts devoted to localizing tourist photos captured by perspective cameras, in this article, we focus on devising person positioning solutions using overhead fisheye cameras. Such solutions are advantageous in large field of view (FOV), low cost, anti-occlusion, and unaggressive work mode (without the necessity of cameras carried by persons). However, related studies are quite scarce, due to the paucity of data. To stimulate research in this exciting area, we present LOAF, the first large-scale overhead fisheye dataset for person detection and localization. LOAF is built with many essential features, e.g., i) the data cover abundant diversities in scenes, human pose, density, and location; ii) it contains currently the largest number of annotated pedestrian, i.e., 457K bounding boxes with groundtruth location information; iii) the body-boxes are labeled as radius-aligned so as to fully address the positioning challenge. To approach localization, we build a fisheye person detection network, which exploits the fisheye distortions by a rotation-equivariant training strategy and predict radius-aligned human boxes end-to-end. Then, the actual locations of the detected persons are calculated by a numerical solution on the fisheye model and camera altitude data. Extensive experiments on LOAF validate the superiority of our fisheye detector w.r.t. previous methods, and show that our whole fisheye positioning solution is able to locate all persons in FOV with an accuracy of 0.5 m, within 0.1 s.