# Multiple Object Tracking by Flowing and Fusing

arXiv: https://arxiv.org/abs/2001.11180 · HTML: https://mlsanity.com/paper/2001.11180/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2020.

## Built on by (most cited first, 17 of 17)

- [Multiple Object Tracking with Correlation Learning](https://mlsanity.com/paper/2104.03541/) (2021)
- [MotionTrack: Learning Robust Short-term and Long-term Motions for Multi-Object Tracking](https://mlsanity.com/paper/2303.10404/) (2023)
- [DiffMOT: A Real-time Diffusion-based Multiple Object Tracker with Non-linear Prediction](https://mlsanity.com/paper/2403.02075/) (2024)
- [GIAOTracker: A comprehensive framework for MCMOT with global information and optimizing strategies in VisDrone 2021](https://mlsanity.com/paper/2202.11983/) (2022)
- [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)
- [Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey](https://mlsanity.com/paper/2205.10766/) (2022)
- [You Only Need Two Detectors to Achieve Multi-Modal 3D Multi-Object Tracking](https://mlsanity.com/paper/2304.08709/) (2023)
- [MPI-Flow: Learning Realistic Optical Flow with Multiplane Images](https://mlsanity.com/paper/2309.06714/) (2023)
- [Single-Shot and Multi-Shot Feature Learning for Multi-Object Tracking](https://mlsanity.com/paper/2311.10382/) (2023)
- [Motion-Aware Vision-Reference Alignment for Referring Multi-Object Tracking](https://mlsanity.com/paper/2511.17681/) (2025)
- [DetFlowTrack: 3D Multi-object Tracking based on Simultaneous Optimization of Object Detection and Scene Flow Estimation](https://mlsanity.com/paper/2203.02157/) (2022)
- [The TYC Dataset for Understanding Instance-Level Semantics and Motions of Cells in Microstructures](https://mlsanity.com/paper/2308.12116/) (2023)
- [Sparse3DTrack: Monocular 3D Object Tracking Using Sparse Supervision](https://mlsanity.com/paper/2603.18298/) (2026)
- [Multi-object Tracking with a Hierarchical Single-branch Network](https://mlsanity.com/paper/2101.01984/) (2021)
- [TeFF: Tracking-enhanced Forgetting-free Few-shot 3D LiDAR Semantic Segmentation](https://mlsanity.com/paper/2408.15657/) (2024)
- [Time-series Meets Complex Motion Modeling: Robust and Computational-effective Motion Predictor for Multi-object Tracking](https://mlsanity.com/paper/2605.00362/) (2026)

## Abstract (arXiv)

Most of Multiple Object Tracking (MOT) approaches compute individual target features for two subtasks: estimating target-wise motions and conducting pair-wise Re-Identification (Re-ID). Because of the indefinite number of targets among video frames, both subtasks are very difficult to scale up efficiently in end-to-end Deep Neural Networks (DNNs). In this paper, we design an end-to-end DNN tracking approach, Flow-Fuse-Tracker (FFT), that addresses the above issues with two efficient techniques: target flowing and target fusing. Specifically, in target flowing, a FlowTracker DNN module learns the indefinite number of target-wise motions jointly from pixel-level optical flows. In target fusing, a FuseTracker DNN module refines and fuses targets proposed by FlowTracker and frame-wise object detection, instead of trusting either of the two inaccurate sources of target proposal. Because FlowTracker can explore complex target-wise motion patterns and FuseTracker can refine and fuse targets from FlowTracker and detectors, our approach can achieve the state-of-the-art results on several MOT benchmarks. As an online MOT approach, FFT produced the top MOTA of 46.3 on the 2DMOT15, 56.5 on the MOT16, and 56.5 on the MOT17 tracking benchmarks, surpassing all the online and offline methods in existing publications.