# View Birdification in the Crowd: Ground-Plane Localization from Perceived Movements

arXiv: https://arxiv.org/abs/2111.05060 · HTML: https://mlsanity.com/paper/2111.05060/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 73 references that are in the map.
- First posted on arXiv 9 Nov 2021.

## Builds on (most cited first, 30 of 35)

- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [MOT16: A Benchmark for Multi-Object Tracking](https://mlsanity.com/paper/1603.00831/) (2016)
- [Towards Real-Time Multi-Object Tracking](https://mlsanity.com/paper/1909.12605/) (2019)
- [Long-term Human Motion Prediction with Scene Context](https://mlsanity.com/paper/2007.03672/) (2020)
- [View Synthesis by Appearance Flow](https://mlsanity.com/paper/1605.03557/) (2016)
- [Learning from Synthetic Data for Crowd Counting in the Wild](https://mlsanity.com/paper/1903.03303/) (2019)
- [DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes](https://mlsanity.com/paper/1806.05620/) (2018)
- [Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation](https://mlsanity.com/paper/1904.06807/) (2019)
- [DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments](https://mlsanity.com/paper/1809.08379/) (2018)
- [Monocular Quasi-Dense 3D Object Tracking](https://mlsanity.com/paper/2103.07351/) (2021)
- [Pose Flow: Efficient Online Pose Tracking](https://mlsanity.com/paper/1802.00977/) (2018)
- [What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction](https://mlsanity.com/paper/1903.07933/) (2019)
- [Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation](https://mlsanity.com/paper/1912.12215/) (2019)
- [Cross-View Image Synthesis using Conditional GANs](https://mlsanity.com/paper/1803.03396/) (2018)
- [Future Person Localization in First-Person Videos](https://mlsanity.com/paper/1711.11217/) (2017)
- [OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association](https://mlsanity.com/paper/2103.02440/) (2021)
- [Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1710.02543/) (2017)
- [MonoLayout: Amodal scene layout from a single image](https://mlsanity.com/paper/2002.08394/) (2020)
- [Body Meshes as Points](https://mlsanity.com/paper/2105.02467/) (2021)
- [Tracking Pedestrian Heads in Dense Crowd](https://mlsanity.com/paper/2103.13516/) (2021)
- [MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation](https://mlsanity.com/paper/1906.06059/) (2019)
- [Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving](https://mlsanity.com/paper/1807.02062/) (2018)
- [Generative Adversarial Frontal View to Bird View Synthesis](https://mlsanity.com/paper/1808.00327/) (2018)
- [Dynamic SLAM: The Need For Speed](https://mlsanity.com/paper/2002.08584/) (2020)
- [Taking a Deeper Look at the Inverse Compositional Algorithm](https://mlsanity.com/paper/1812.06861/) (2018)
- [General Dynamic Scene Reconstruction from Multiple View Video](https://mlsanity.com/paper/1509.09294/) (2015)
- [Multimodal Future Localization and Emergence Prediction for Objects in Egocentric View with a Reachability Prior](https://mlsanity.com/paper/2006.04700/) (2020)
- [Ego2Top: Matching Viewers in Egocentric and Top-view Videos](https://mlsanity.com/paper/1607.06986/) (2016)
- [ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings](https://mlsanity.com/paper/2003.12980/) (2020)
- [PillarFlow: End-to-end Birds-eye-view Flow Estimation for Autonomous Driving](https://mlsanity.com/paper/2008.01179/) (2020)

## Built on by (most cited first, 4 of 4)

- [Bridging Perspectives: A Survey on Cross-view Collaborative Intelligence with Egocentric-Exocentric Vision](https://mlsanity.com/paper/2506.06253/) (2025)
- [Egocentric and Exocentric Methods: A Short Survey](https://mlsanity.com/paper/2410.20621/) (2024)
- [ViewBirdiformer: Learning to recover ground-plane crowd trajectories and ego-motion from a single ego-centric view](https://mlsanity.com/paper/2210.06332/) (2022)
- [InCrowdFormer: On-Ground Pedestrian World Model From Egocentric Views](https://mlsanity.com/paper/2303.09534/) (2023)

## Abstract (arXiv)

We introduce view birdification, the problem of recovering ground-plane movements of people in a crowd from an ego-centric video captured from an observer (e.g., a person or a vehicle) also moving in the crowd. Recovered ground-plane movements would provide a sound basis for situational understanding and benefit downstream applications in computer vision and robotics. In this paper, we formulate view birdification as a geometric trajectory reconstruction problem and derive a cascaded optimization method from a Bayesian perspective. The method first estimates the observer's movement and then localizes surrounding pedestrians for each frame while taking into account the local interactions between them. We introduce three datasets by leveraging synthetic and real trajectories of people in crowds and evaluate the effectiveness of our method. The results demonstrate the accuracy of our method and set the ground for further studies of view birdification as an important but challenging visual understanding problem.