# Scalable Detection and Tracking of Geometric Extended Objects

arXiv: https://arxiv.org/abs/2103.11279 · HTML: https://mlsanity.com/paper/2103.11279/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2021.

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

- [Neural Enhanced Belief Propagation for Multiobject Tracking](https://mlsanity.com/paper/2212.08340/) (2022)
- [Neural Enhanced Belief Propagation for Data Association in Multiobject Tracking](https://mlsanity.com/paper/2203.09948/) (2022)
- [DISTA-Net: Dynamic Closely-Spaced Infrared Small Target Unmixing](https://mlsanity.com/paper/2505.19148/) (2025)
- [A New Architecture for Neural Enhanced Multiobject Tracking](https://mlsanity.com/paper/2410.06294/) (2024)
- [Bayesian Multiobject Tracking With Neural-Enhanced Motion and Measurement Models](https://mlsanity.com/paper/2506.18124/) (2025)
- [OptiPMB: Enhancing 3D Multi-Object Tracking with Optimized Poisson Multi-Bernoulli Filtering](https://mlsanity.com/paper/2503.12968/) (2025)
- [Seamless Tracking of Group Targets and Ungrouped Targets Using Belief Propagation](https://mlsanity.com/paper/2208.12035/) (2022)
- [Classification-Aided Robust Multiple Target Tracking Using Neural Enhanced Message Passing](https://mlsanity.com/paper/2310.12407/) (2023)
- [3D Extended Object Tracking based on Extruded B-Spline Side View Profiles](https://mlsanity.com/paper/2503.10730/) (2025)
- [Integrated Gaussian Processes for Robust and Adaptive Multi-Object Tracking](https://mlsanity.com/paper/2507.04116/) (2025)
- [PiVoT: A Variational Solution for Real-time Large-scale Multi-object Detection and Tracking under Heavy Clutter](https://mlsanity.com/paper/2607.13891/) (2026)
- [DISTA-Net++: Rethinking Infrared Small Target Unmixing Beyond Sub-Pixel Separation](https://mlsanity.com/paper/2609.18773/) (2026)

## Abstract (arXiv)

Multiobject tracking provides situational awareness that enables new applications for modern convenience, public safety, and homeland security. This paper presents a factor graph formulation and a particle-based sum-product algorithm (SPA) for scalable detection and tracking of extended objects. The proposed method dynamically introduces states of newly detected objects, efficiently performs probabilistic multiple-measurement to object association, and jointly infers the geometric shapes of objects. Scalable extended object tracking (EOT) is enabled by modeling association uncertainty by measurement-oriented association variables and newly detected objects by a Poisson birth process. Contrary to conventional EOT methods, a fully particle-based approach makes it possible to describe different geometric object shapes. The proposed method can reliably detect, localize, and track a large number of closely-spaced extended objects without gating and clustering of measurements. We demonstrate significant performance advantages of our approach compared to the recently introduced Poisson multi-Bernoulli mixture filter. In particular, we consider a simulated scenarios with up to twenty closely-spaced objects and a real autonomous driving application where measurements are captured by a lidar sensor.