# Online Multi-Target Tracking Using Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1604.03635 · HTML: https://mlsanity.com/paper/1604.03635/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2016.

## Built on by (most cited first, 30 of 59)

- [Learning to Track with Object Permanence](https://mlsanity.com/paper/2103.14258/) (2021)
- [TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking](https://mlsanity.com/paper/2104.00194/) (2021)
- [Hard to Track Objects with Irregular Motions and Similar Appearances? Make It Easier by Buffering the Matching Space](https://mlsanity.com/paper/2211.14317/) (2022)
- [Unifying Short and Long-Term Tracking with Graph Hierarchies](https://mlsanity.com/paper/2212.03038/) (2022)
- [QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Tracking](https://mlsanity.com/paper/2210.06984/) (2022)
- [OVTrack: Open-Vocabulary Multiple Object Tracking](https://mlsanity.com/paper/2304.08408/) (2023)
- [GIAOTracker: A comprehensive framework for MCMOT with global information and optimizing strategies in VisDrone 2021](https://mlsanity.com/paper/2202.11983/) (2022)
- [Tracking Every Thing in the Wild](https://mlsanity.com/paper/2207.12978/) (2022)
- [TransCenter: Transformers with Dense Representations for Multiple-Object Tracking](https://mlsanity.com/paper/2103.15145/) (2021)
- [Deep Learning for UAV-based Object Detection and Tracking: A Survey](https://mlsanity.com/paper/2110.12638/) (2021)
- [Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking](https://mlsanity.com/paper/2101.12159/) (2021)
- [MotionTrack: Learning Motion Predictor for Multiple Object Tracking](https://mlsanity.com/paper/2306.02585/) (2023)
- [Split and Connect: A Universal Tracklet Booster for Multi-Object Tracking](https://mlsanity.com/paper/2105.02426/) (2021)
- [FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow](https://mlsanity.com/paper/2308.07207/) (2023)
- [Online Multiple Object Tracking with Cross-Task Synergy](https://mlsanity.com/paper/2104.00380/) (2021)
- [In Pursuit of Many: A Review of Modern Multiple Object Tracking Systems](https://mlsanity.com/paper/2209.04796/) (2022)
- [Learning of Global Objective for Network Flow in Multi-Object Tracking](https://mlsanity.com/paper/2203.16210/) (2022)
- [Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planning](https://mlsanity.com/paper/2306.01980/) (2023)
- [ETTrack: Enhanced Temporal Motion Predictor for Multi-Object Tracking](https://mlsanity.com/paper/2405.15755/) (2024)
- [Multiple Object Tracking with Mixture Density Networks for Trajectory Estimation](https://mlsanity.com/paper/2106.10950/) (2021)
- [Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encoding](https://mlsanity.com/paper/2202.01478/) (2022)
- [Scenario-Assisted Deep Reinforcement Learning](https://mlsanity.com/paper/2202.04337/) (2022)
- [Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey](https://mlsanity.com/paper/2205.10766/) (2022)
- [Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting](https://mlsanity.com/paper/2101.02385/) (2021)
- [Towards Frame Rate Agnostic Multi-Object Tracking](https://mlsanity.com/paper/2209.11404/) (2022)
- [Bridging Images and Videos: A Simple Learning Framework for Large Vocabulary Video Object Detection](https://mlsanity.com/paper/2212.10147/) (2022)
- [Can Deep Learning be Applied to Model-Based Multi-Object Tracking?](https://mlsanity.com/paper/2202.07909/) (2022)
- [Deep Learning-Based Robust Multi-Object Tracking via Fusion of mmWave Radar and Camera Sensors](https://mlsanity.com/paper/2407.08049/) (2024)
- [Simple online and real-time tracking with occlusion handling](https://mlsanity.com/paper/2103.04147/) (2021)
- [Transformer-Based Multi-Object Smoothing with Decoupled Data Association and Smoothing](https://mlsanity.com/paper/2312.17261/) (2023)

## Abstract (arXiv)

We present a novel approach to online multi-target tracking based on recurrent neural networks (RNNs). Tracking multiple objects in real-world scenes involves many challenges, including a) an a-priori unknown and time-varying number of targets, b) a continuous state estimation of all present targets, and c) a discrete combinatorial problem of data association. Most previous methods involve complex models that require tedious tuning of parameters. Here, we propose for the first time, an end-to-end learning approach for online multi-target tracking. Existing deep learning methods are not designed for the above challenges and cannot be trivially applied to the task. Our solution addresses all of the above points in a principled way. Experiments on both synthetic and real data show promising results obtained at ~300 Hz on a standard CPU, and pave the way towards future research in this direction.