# Multi-Object Tracking based on Imaging Radar 3D Object Detection

arXiv: https://arxiv.org/abs/2406.01011 · HTML: https://mlsanity.com/paper/2406.01011/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 3 Jun 2024.

## Builds on (most cited first, 18 of 18)

- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking](https://mlsanity.com/paper/2009.07736/) (2020)
- [Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection](https://mlsanity.com/paper/2012.15712/) (2020)
- [PnPNet: End-to-End Perception and Prediction with Tracking in the Loop](https://mlsanity.com/paper/2005.14711/) (2020)
- [DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets](https://mlsanity.com/paper/2301.06051/) (2023)
- [A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving](https://mlsanity.com/paper/2011.10671/) (2020)
- [SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking](https://mlsanity.com/paper/2111.09621/) (2021)
- [TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving](https://mlsanity.com/paper/2204.13483/) (2022)
- [Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving](https://mlsanity.com/paper/2012.13755/) (2020)
- [Probabilistic 3D Multi-Object Tracking for Autonomous Driving](https://mlsanity.com/paper/2001.05673/) (2020)
- [RaTrack: Moving Object Detection and Tracking with 4D Radar Point Cloud](https://mlsanity.com/paper/2309.09737/) (2023)
- [Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection](https://mlsanity.com/paper/2211.13067/) (2022)
- [Which Framework is Suitable for Online 3D Multi-Object Tracking for Autonomous Driving with Automotive 4D Imaging Radar?](https://mlsanity.com/paper/2309.06036/) (2023)
- [Reviewing 3D Object Detectors in the Context of High-Resolution 3+1D Radar](https://mlsanity.com/paper/2308.05478/) (2023)
- [InterTrack: Interaction Transformer for 3D Multi-Object Tracking](https://mlsanity.com/paper/2208.08041/) (2022)
- [Ego-Motion Estimation and Dynamic Motion Separation from 3D Point Clouds for Accumulating Data and Improving 3D Object Detection](https://mlsanity.com/paper/2308.15357/) (2023)
- [A Complete Derivation Of The Association Log-Likelihood Distance For Multi-Object Tracking](https://mlsanity.com/paper/1508.04124/) (2015)

## Abstract (arXiv)

Effective tracking of surrounding traffic participants allows for an accurate state estimation as a necessary ingredient for prediction of future behavior and therefore adequate planning of the ego vehicle trajectory. One approach for detecting and tracking surrounding traffic participants is the combination of a learning based object detector with a classical tracking algorithm. Learning based object detectors have been shown to work adequately on lidar and camera data, while learning based object detectors using standard radar data input have proven to be inferior. Recently, with the improvements to radar sensor technology in the form of imaging radars, the object detection performance on radar was greatly improved but is still limited compared to lidar sensors due to the sparsity of the radar point cloud. This presents a unique challenge for the task of multi-object tracking. The tracking algorithm must overcome the limited detection quality while generating consistent tracks. To this end, a comparison between different multi-object tracking methods on imaging radar data is required to investigate its potential for downstream tasks. The work at hand compares multiple approaches and analyzes their limitations when applied to imaging radar data. Furthermore, enhancements to the presented approaches in the form of probabilistic association algorithms are considered for this task.