# Simultaneous Detection and Tracking with Motion Modelling for Multiple Object Tracking

arXiv: https://arxiv.org/abs/2008.08826 · HTML: https://mlsanity.com/paper/2008.08826/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2020.

## Built on by (most cited first, 25 of 25)

- [MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?](https://mlsanity.com/paper/2108.09518/) (2021)
- [Opening up Open-World Tracking](https://mlsanity.com/paper/2104.11221/) (2021)
- [Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction](https://mlsanity.com/paper/2303.16005/) (2023)
- [GIAOTracker: A comprehensive framework for MCMOT with global information and optimizing strategies in VisDrone 2021](https://mlsanity.com/paper/2202.11983/) (2022)
- [Tracking Objects as Pixel-wise Distributions](https://mlsanity.com/paper/2207.05518/) (2022)
- [DyGLIP: A Dynamic Graph Model with Link Prediction for Accurate Multi-Camera Multiple Object Tracking](https://mlsanity.com/paper/2106.06856/) (2021)
- [ReST: A Reconfigurable Spatial-Temporal Graph Model for Multi-Camera Multi-Object Tracking](https://mlsanity.com/paper/2308.13229/) (2023)
- [Region Aware Video Object Segmentation with Deep Motion Modeling](https://mlsanity.com/paper/2207.10258/) (2022)
- [Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey](https://mlsanity.com/paper/2205.10766/) (2022)
- [Depth Perspective-aware Multiple Object Tracking](https://mlsanity.com/paper/2207.04551/) (2022)
- [Z-GMOT: Zero-shot Generic Multiple Object Tracking](https://mlsanity.com/paper/2305.17648/) (2023)
- [SPAMming Labels: Efficient Annotations for the Trackers of Tomorrow](https://mlsanity.com/paper/2404.11426/) (2024)
- [Global Correlation Network: End-to-End Joint Multi-Object Detection and Tracking](https://mlsanity.com/paper/2103.12511/) (2021)
- [Can Deep Learning be Applied to Model-Based Multi-Object Tracking?](https://mlsanity.com/paper/2202.07909/) (2022)
- [Transformer-Based Multi-Object Smoothing with Decoupled Data Association and Smoothing](https://mlsanity.com/paper/2312.17261/) (2023)
- [TP-GMOT: Tracking Generic Multiple Object by Textual Prompt with Motion-Appearance Cost (MAC) SORT](https://mlsanity.com/paper/2409.02490/) (2024)
- [UTOPIA: Unconstrained Tracking Objects without Preliminary Examination via Cross-Domain Adaptation](https://mlsanity.com/paper/2306.09613/) (2023)
- [Enhanced Kalman with Adaptive Appearance Motion SORT for Grounded Generic Multiple Object Tracking](https://mlsanity.com/paper/2410.09243/) (2024)
- [DINTR: Tracking via Diffusion-based Interpolation](https://mlsanity.com/paper/2410.10053/) (2024)
- [Localization-Based Tracking](https://mlsanity.com/paper/2104.05823/) (2021)
- [Reliable Multi-Object Tracking in the Presence of Unreliable Detections](https://mlsanity.com/paper/2112.08345/) (2021)
- [PieTrack: An MOT solution based on synthetic data training and self-supervised domain adaptation](https://mlsanity.com/paper/2207.11325/) (2022)
- [OSRE: Object-to-Spot Rotation Estimation for Bike Parking Assessment](https://mlsanity.com/paper/2303.00725/) (2023)
- [Into the Fog: Evaluating Robustness of Multiple Object Tracking](https://mlsanity.com/paper/2404.10534/) (2024)
- [R3ST: A Synthetic 3D Dataset With Realistic Trajectories](https://mlsanity.com/paper/2512.16784/) (2025)

## Abstract (arXiv)

Deep learning-based Multiple Object Tracking (MOT) currently relies on off-the-shelf detectors for tracking-by-detection.This results in deep models that are detector biased and evaluations that are detector influenced. To resolve this issue, we introduce Deep Motion Modeling Network (DMM-Net) that can estimate multiple objects' motion parameters to perform joint detection and association in an end-to-end manner. DMM-Net models object features over multiple frames and simultaneously infers object classes, visibility, and their motion parameters. These outputs are readily used to update the tracklets for efficient MOT. DMM-Net achieves PR-MOTA score of 12.80 @ 120+ fps for the popular UA-DETRAC challenge, which is better performance and orders of magnitude faster. We also contribute a synthetic large-scale public dataset Omni-MOT for vehicle tracking that provides precise ground-truth annotations to eliminate the detector influence in MOT evaluation. This 14M+ frames dataset is extendable with our public script (Code at Dataset <https://github.com/shijieS/OmniMOTDataset>, Dataset Recorder <https://github.com/shijieS/OMOTDRecorder>, Omni-MOT Source <https://github.com/shijieS/DMMN>). We demonstrate the suitability of Omni-MOT for deep learning with DMMNet and also make the source code of our network public.