# IMM-MOT: A Novel 3D Multi-object Tracking Framework with Interacting Multiple Model Filter

arXiv: https://arxiv.org/abs/2502.09672 · HTML: https://mlsanity.com/paper/2502.09672/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 27 references that are in the map.
- First posted on arXiv 13 Feb 2025.

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

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [Soft-NMS -- Improving Object Detection With One Line of Code](https://mlsanity.com/paper/1704.04503/) (2017)
- [SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking](https://mlsanity.com/paper/2111.09621/) (2021)
- [EagerMOT: 3D Multi-Object Tracking via Sensor Fusion](https://mlsanity.com/paper/2104.14682/) (2021)
- [LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs](https://mlsanity.com/paper/2206.10555/) (2022)
- [Adaptive NMS: Refining Pedestrian Detection in a Crowd](https://mlsanity.com/paper/1904.03629/) (2019)
- [Learnable Online Graph Representations for 3D Multi-Object Tracking](https://mlsanity.com/paper/2104.11747/) (2021)
- [Score refinement for confidence-based 3D multi-object tracking](https://mlsanity.com/paper/2107.04327/) (2021)
- [Poly-MOT: A Polyhedral Framework For 3D Multi-Object Tracking](https://mlsanity.com/paper/2307.16675/) (2023)
- [Joint Multi-Object Detection and Tracking with Camera-LiDAR Fusion for Autonomous Driving](https://mlsanity.com/paper/2108.04602/) (2021)
- [ShaSTA: Modeling Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking](https://mlsanity.com/paper/2211.03919/) (2022)
- [MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving](https://mlsanity.com/paper/2409.16149/) (2024)
- [BoostTrack++: using tracklet information to detect more objects in multiple object tracking](https://mlsanity.com/paper/2408.13003/) (2024)
- [RobMOT: Robust 3D Multi-Object Tracking by Observational Noise and State Estimation Drift Mitigation on LiDAR PointCloud](https://mlsanity.com/paper/2405.11536/) (2024)
- [SWTrack: Multiple Hypothesis Sliding Window 3D Multi-Object Tracking](https://mlsanity.com/paper/2402.17892/) (2024)
- [Enhancing Thermal MOT: A Novel Box Association Method Leveraging Thermal Identity and Motion Similarity](https://mlsanity.com/paper/2411.12943/) (2024)
- [PKF: Probabilistic Data Association Kalman Filter for Multi-Object Tracking](https://mlsanity.com/paper/2411.06378/) (2024)
- [Enhanced Kalman with Adaptive Appearance Motion SORT for Grounded Generic Multiple Object Tracking](https://mlsanity.com/paper/2410.09243/) (2024)

## Built on by (most cited first, 2 of 2)

- [Radar-Informed 3D Multi-Object Tracking under Adverse Conditions](https://mlsanity.com/paper/2604.13571/) (2026)
- [IMM-based Multiple Object Tracking using a State Prediction Neural Network](https://mlsanity.com/paper/2609.13307/) (2026)

## Abstract (arXiv)

3D Multi-Object Tracking (MOT) provides the trajectories of surrounding objects, assisting robots or vehicles in smarter path planning and obstacle avoidance. Existing 3D MOT methods based on the Tracking-by-Detection framework typically use a single motion model to track an object throughout its entire tracking process. However, objects may change their motion patterns due to variations in the surrounding environment. In this paper, we introduce the Interacting Multiple Model filter in IMM-MOT, which accurately fits the complex motion patterns of individual objects, overcoming the limitation of single-model tracking in existing approaches. In addition, we incorporate a Damping Window mechanism into the trajectory lifecycle management, leveraging the continuous association status of trajectories to control their creation and termination, reducing the occurrence of overlooked low-confidence true targets. Furthermore, we propose the Distance-Based Score Enhancement module, which enhances the differentiation between false positives and true positives by adjusting detection scores, thereby improving the effectiveness of the Score Filter. On the NuScenes Val dataset, IMM-MOT outperforms most other single-modal models using 3D point clouds, achieving an AMOTA of 73.8%. Our project is available at https://github.com/Ap01lo/IMM-MOT.