# Multi-view Tracking Using Weakly Supervised Human Motion Prediction

arXiv: https://arxiv.org/abs/2210.10771 · HTML: https://mlsanity.com/paper/2210.10771/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 65 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [Tracking Objects as Points](https://mlsanity.com/paper/2004.01177/) (2020)
- [Quasi-Dense Similarity Learning for Multiple Object Tracking](https://mlsanity.com/paper/2006.06664/) (2020)
- [Learning to Track with Object Permanence](https://mlsanity.com/paper/2103.14258/) (2021)
- [Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification](https://mlsanity.com/paper/1809.04427/) (2018)
- [How To Train Your Deep Multi-Object Tracker](https://mlsanity.com/paper/1906.06618/) (2019)
- [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)
- [Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection](https://mlsanity.com/paper/1704.05775/) (2017)
- [Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking](https://mlsanity.com/paper/2012.02337/) (2020)
- [Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking](https://mlsanity.com/paper/2101.12159/) (2021)
- [Estimating People Flows to Better Count Them in Crowded Scenes](https://mlsanity.com/paper/1911.10782/) (2019)
- [Counting People by Estimating People Flows](https://mlsanity.com/paper/2012.00452/) (2020)
- [The WILDTRACK Multi-Camera Person Dataset](https://mlsanity.com/paper/1707.09299/) (2017)
- [FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation](https://mlsanity.com/paper/1407.6251/) (2014)
- [Eliminating Exposure Bias and Loss-Evaluation Mismatch in Multiple Object Tracking](https://mlsanity.com/paper/1811.10984/) (2018)
- [Multiple Hypothesis Tracking Algorithm for Multi-Target Multi-Camera Tracking with Disjoint Views](https://mlsanity.com/paper/1901.08787/) (2019)

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

- [Lifting Multi-View Detection and Tracking to the Bird's Eye View](https://mlsanity.com/paper/2403.12573/) (2024)
- [ReST: A Reconfigurable Spatial-Temporal Graph Model for Multi-Camera Multi-Object Tracking](https://mlsanity.com/paper/2308.13229/) (2023)
- [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)
- [Attention-Aware Multi-View Pedestrian Tracking](https://mlsanity.com/paper/2504.03047/) (2025)
- [One Graph to Track Them All: Dynamic GNNs for Single- and Multi-View Tracking](https://mlsanity.com/paper/2507.08494/) (2025)
- [SAVeD: Learning to Denoise Low-SNR Video for Improved Downstream Performance](https://mlsanity.com/paper/2504.00161/) (2025)
- [Multi Camera Connected Vision System with Multi View Analytics: A Comprehensive Survey](https://mlsanity.com/paper/2510.09731/) (2025)
- [Multi-view Crowd Tracking Transformer with View-Ground Interactions Under Large Real-World Scenes](https://mlsanity.com/paper/2604.19318/) (2026)
- [Fully Distributed Multi-View 3D Tracking in Real-Time](https://mlsanity.com/paper/2606.13127/) (2026)

## Abstract (arXiv)

Multi-view approaches to people-tracking have the potential to better handle occlusions than single-view ones in crowded scenes. They often rely on the tracking-by-detection paradigm, which involves detecting people first and then connecting the detections. In this paper, we argue that an even more effective approach is to predict people motion over time and infer people's presence in individual frames from these. This enables to enforce consistency both over time and across views of a single temporal frame. We validate our approach on the PETS2009 and WILDTRACK datasets and demonstrate that it outperforms state-of-the-art methods.