# Bringing Generalization to Deep Multi-View Pedestrian Detection

arXiv: https://arxiv.org/abs/2109.12227 · HTML: https://mlsanity.com/paper/2109.12227/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 36 references that are in the map.
- First posted on arXiv 24 Sep 2021.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [What do different evaluation metrics tell us about saliency models?](https://mlsanity.com/paper/1604.03605/) (2016)
- [Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification](https://mlsanity.com/paper/2012.00417/) (2020)
- [Dissecting Person Re-identification from the Viewpoint of Viewpoint](https://mlsanity.com/paper/1812.02162/) (2018)
- [Multiview Detection with Feature Perspective Transformation](https://mlsanity.com/paper/2007.07247/) (2020)
- [Multiview Detection with Shadow Transformer (and View-Coherent Data Augmentation)](https://mlsanity.com/paper/2108.05888/) (2021)
- [Self-Supervised Feature Learning by Learning to Spot Artifacts](https://mlsanity.com/paper/1806.05024/) (2018)
- [ViNet: Pushing the limits of Visual Modality for Audio-Visual Saliency Prediction](https://mlsanity.com/paper/2012.06170/) (2020)
- [Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection](https://mlsanity.com/paper/1704.05775/) (2017)
- [Deep Multi-camera People Detection](https://mlsanity.com/paper/1702.04593/) (2017)
- [FoveaBox: Beyond Anchor-based Object Detector](https://mlsanity.com/paper/1904.03797/) (2019)
- [Self-supervised Learning for Video Correspondence Flow](https://mlsanity.com/paper/1905.00875/) (2019)
- [Tidying Deep Saliency Prediction Architectures](https://mlsanity.com/paper/2003.04942/) (2020)
- [mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets](https://mlsanity.com/paper/2012.08385/) (2020)
- [Generalizable Multi-Camera 3D Pedestrian Detection](https://mlsanity.com/paper/2104.05813/) (2021)
- [Semantic Driven Multi-Camera Pedestrian Detection](https://mlsanity.com/paper/1812.10779/) (2018)

## Built on by (most cited first, 11 of 11)

- [Enhanced Multi-View Pedestrian Detection Using Probabilistic Occupancy Volume](https://mlsanity.com/paper/2503.10982/) (2025)
- [DCHM: Depth-Consistent Human Modeling for Multiview Detection](https://mlsanity.com/paper/2507.14505/) (2025)
- [MVTrajecter: Multi-View Pedestrian Tracking with Trajectory Motion Cost and Trajectory Appearance Cost](https://mlsanity.com/paper/2509.01157/) (2025)
- [MVUDA: Unsupervised Domain Adaptation for Multi-view Pedestrian Detection](https://mlsanity.com/paper/2412.04117/) (2024)
- [MC-BEVRO: Multi-Camera Bird Eye View Road Occupancy Detection for Traffic Monitoring](https://mlsanity.com/paper/2502.11287/) (2025)
- [Toward unlabeled multi-view 3D pedestrian detection by generalizable AI: techniques and performance analysis](https://mlsanity.com/paper/2308.04515/) (2023)
- [MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection](https://mlsanity.com/paper/2508.20447/) (2025)
- [Multi-view Crowd Tracking Transformer with View-Ground Interactions Under Large Real-World Scenes](https://mlsanity.com/paper/2604.19318/) (2026)
- [MVDGC: Joint 3D and 2D Multi-view Pedestrian Detection via Dual Geometric Constraints](https://mlsanity.com/paper/2607.00273/) (2026)
- [MV2GF: Multi-view Pedestrian Detection with a Visual Geometric Foundation Model](https://mlsanity.com/paper/2608.20639/) (2026)
- [CAT-Free: Multi-View Pedestrian Localization without Calibration, Annotations, or Target-Scene Training via Adaptive Geometric Filtering](https://mlsanity.com/paper/2609.34302/) (2026)

## Abstract (arXiv)

Multi-view Detection (MVD) is highly effective for occlusion reasoning in a crowded environment. While recent works using deep learning have made significant advances in the field, they have overlooked the generalization aspect, which makes them impractical for real-world deployment. The key novelty of our work is to formalize three critical forms of generalization and propose experiments to evaluate them: generalization with i) a varying number of cameras, ii) varying camera positions, and finally, iii) to new scenes. We find that existing state-of-the-art models show poor generalization by overfitting to a single scene and camera configuration. To address the concerns: (a) we propose a novel Generalized MVD (GMVD) dataset, assimilating diverse scenes with changing daytime, camera configurations, varying number of cameras, and (b) we discuss the properties essential to bring generalization to MVD and propose a barebones model to incorporate them. We perform a comprehensive set of experiments on the WildTrack, MultiViewX, and the GMVD datasets to motivate the necessity to evaluate the generalization abilities of MVD methods and to demonstrate the efficacy of the proposed approach. The code and the proposed dataset can be found at https://github.com/jeetv/GMVD