# MAT: Motion-Aware Multi-Object Tracking

arXiv: https://arxiv.org/abs/2009.04794 · HTML: https://mlsanity.com/paper/2009.04794/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2020.

## Built on by (most cited first, 30 of 40)

- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [BoT-SORT: Robust Associations Multi-Pedestrian Tracking](https://mlsanity.com/paper/2206.14651/) (2022)
- [StrongSORT: Make DeepSORT Great Again](https://mlsanity.com/paper/2202.13514/) (2022)
- [TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking](https://mlsanity.com/paper/2104.00194/) (2021)
- [MotionTrack: Learning Robust Short-term and Long-term Motions for Multi-Object Tracking](https://mlsanity.com/paper/2303.10404/) (2023)
- [Do Different Tracking Tasks Require Different Appearance Models?](https://mlsanity.com/paper/2107.02156/) (2021)
- [RelationTrack: Relation-aware Multiple Object Tracking with Decoupled Representation](https://mlsanity.com/paper/2105.04322/) (2021)
- [SparseTrack: Multi-Object Tracking by Performing Scene Decomposition based on Pseudo-Depth](https://mlsanity.com/paper/2306.05238/) (2023)
- [GIAOTracker: A comprehensive framework for MCMOT with global information and optimizing strategies in VisDrone 2021](https://mlsanity.com/paper/2202.11983/) (2022)
- [TransCenter: Transformers with Dense Representations for Multiple-Object Tracking](https://mlsanity.com/paper/2103.15145/) (2021)
- [MOTRv3: Release-Fetch Supervision for End-to-End Multi-Object Tracking](https://mlsanity.com/paper/2305.14298/) (2023)
- [Towards Discriminative Representation: Multi-view Trajectory Contrastive Learning for Online Multi-object Tracking](https://mlsanity.com/paper/2203.14208/) (2022)
- [iKUN: Speak to Trackers without Retraining](https://mlsanity.com/paper/2312.16245/) (2023)
- [SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking](https://mlsanity.com/paper/2211.08824/) (2022)
- [Focus On Details: Online Multi-object Tracking with Diverse Fine-grained Representation](https://mlsanity.com/paper/2302.14589/) (2023)
- [Towards Generalizable Multi-Object Tracking](https://mlsanity.com/paper/2406.00429/) (2024)
- [MotionTrack: Learning Motion Predictor for Multiple Object Tracking](https://mlsanity.com/paper/2306.02585/) (2023)
- [Delving into the Trajectory Long-tail Distribution for Muti-object Tracking](https://mlsanity.com/paper/2403.04700/) (2024)
- [Quality Matters: Embracing Quality Clues for Robust 3D Multi-Object Tracking](https://mlsanity.com/paper/2208.10976/) (2022)
- [DiffSynth: Latent In-Iteration Deflickering for Realistic Video Synthesis](https://mlsanity.com/paper/2308.03463/) (2023)
- [BronchoTrack: Airway Lumen Tracking for Branch-Level Bronchoscopic Localization](https://mlsanity.com/paper/2402.12763/) (2024)
- [DeconfuseTrack:Dealing with Confusion for Multi-Object Tracking](https://mlsanity.com/paper/2403.02767/) (2024)
- [Local Metrics for Multi-Object Tracking](https://mlsanity.com/paper/2104.02631/) (2021)
- [FeatureSORT: Essential Features for Effective Tracking](https://mlsanity.com/paper/2407.04249/) (2024)
- [SimpleTrack: Rethinking and Improving the JDE Approach for Multi-Object Tracking](https://mlsanity.com/paper/2203.03985/) (2022)
- [AttMOT: Improving Multiple-Object Tracking by Introducing Auxiliary Pedestrian Attributes](https://mlsanity.com/paper/2308.07537/) (2023)
- [SpikeMOT: Event-based Multi-Object Tracking with Sparse Motion Features](https://mlsanity.com/paper/2309.16987/) (2023)
- [PatchTrack: Multiple Object Tracking Using Frame Patches](https://mlsanity.com/paper/2201.00080/) (2022)
- [End-to-end Tracking with a Multi-query Transformer](https://mlsanity.com/paper/2210.14601/) (2022)
- [Single-Shot and Multi-Shot Feature Learning for Multi-Object Tracking](https://mlsanity.com/paper/2311.10382/) (2023)

## Abstract (arXiv)

Modern multi-object tracking (MOT) systems usually model the trajectories by associating per-frame detections. However, when camera motion, fast motion, and occlusion challenges occur, it is difficult to ensure long-range tracking or even the tracklet purity, especially for small objects. Although re-identification is often employed, due to noisy partial-detections, similar appearance, and lack of temporal-spatial constraints, it is not only unreliable and time-consuming, but still cannot address the false negatives for occluded and blurred objects. In this paper, we propose an enhanced MOT paradigm, namely Motion-Aware Tracker (MAT), focusing more on various motion patterns of different objects. The rigid camera motion and nonrigid pedestrian motion are blended compatibly to form the integrated motion localization module. Meanwhile, we introduce the dynamic reconnection context module, which aims to balance the robustness of long-range motion-based reconnection, and includes the cyclic pseudo-observation updating strategy to smoothly fill in the tracking fragments caused by occlusion or blur. Additionally, the 3D integral image module is presented to efficiently cut useless track-detection association connections with temporal-spatial constraints. Extensive experiments on MOT16 and MOT17 challenging benchmarks demonstrate that our MAT approach can achieve the superior performance by a large margin with high efficiency, in contrast to other state-of-the-art trackers.