# ETTrack: Enhanced Temporal Motion Predictor for Multi-Object Tracking arXiv: https://arxiv.org/abs/2405.15755 ยท HTML: https://mlsanity.com/paper/2405.15755/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 44 of its 58 references that are in the map. - First posted on arXiv 24 May 2024. ## Builds on (most cited first, 30 of 44) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020) - [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016) - [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019) - [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019) - [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020) - [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018) - [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021) - [CvT: Introducing Convolutions to Vision Transformers](https://mlsanity.com/paper/2103.15808/) (2021) - [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017) - [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016) - [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021) - [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016) - [HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking](https://mlsanity.com/paper/2009.07736/) (2020) - [MOT16: A Benchmark for Multi-Object Tracking](https://mlsanity.com/paper/1603.00831/) (2016) - [TrackFormer: Multi-Object Tracking with Transformers](https://mlsanity.com/paper/2101.02702/) (2021) - [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020) - [Tracking Objects as Points](https://mlsanity.com/paper/2004.01177/) (2020) - [MOTR: End-to-End Multiple-Object Tracking with Transformer](https://mlsanity.com/paper/2105.03247/) (2021) - [TransTrack: Multiple Object Tracking with Transformer](https://mlsanity.com/paper/2012.15460/) (2020) - [Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking](https://mlsanity.com/paper/2203.14360/) (2022) - [BoT-SORT: Robust Associations Multi-Pedestrian Tracking](https://mlsanity.com/paper/2206.14651/) (2022) - [Towards Real-Time Multi-Object Tracking](https://mlsanity.com/paper/1909.12605/) (2019) - [Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net](https://mlsanity.com/paper/2012.12395/) (2020) - [DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion](https://mlsanity.com/paper/2111.14690/) (2021) - [StrongSORT: Make DeepSORT Great Again](https://mlsanity.com/paper/2202.13514/) (2022) - [Track to Detect and Segment: An Online Multi-Object Tracker](https://mlsanity.com/paper/2103.08808/) (2021) - [GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras](https://mlsanity.com/paper/2112.01524/) (2021) ## Built on by (most cited first, 9 of 9) - [TrackSSM: A General Motion Predictor by State-Space Model](https://mlsanity.com/paper/2409.00487/) (2024) - [CAMELTrack: Context-Aware Multi-cue ExpLoitation for Online Multi-Object Tracking](https://mlsanity.com/paper/2505.01257/) (2025) - [Deep Learning-Based Multi-Object Tracking: A Comprehensive Survey from Foundations to State-of-the-Art](https://mlsanity.com/paper/2506.13457/) (2025) - [NOOUGAT: Towards Unified Online and Offline Multi-Object Tracking](https://mlsanity.com/paper/2509.02111/) (2025) - [Hypergraph-State Collaborative Reasoning for Multi-Object Tracking](https://mlsanity.com/paper/2604.12665/) (2026) - [GRASPTrack: Geometry-Reasoned Association via Segmentation and Projection for Multi-Object Tracking](https://mlsanity.com/paper/2508.08117/) (2025) - [Occlusion-Aware SORT: Observing Occlusion for Robust Multi-Object Tracking](https://mlsanity.com/paper/2603.06034/) (2026) - [Tracking by Detection and Query: An Efficient End-to-End Framework for Multi-Object Tracking](https://mlsanity.com/paper/2411.06197/) (2024) - [Learning Association via Track-Detection Matching for Multi-Object Tracking](https://mlsanity.com/paper/2512.22105/) (2025) ## Abstract (arXiv) Many Multi-Object Tracking (MOT) approaches exploit motion information to associate all the detected objects across frames. However, many methods that rely on filtering-based algorithms, such as the Kalman Filter, often work well in linear motion scenarios but struggle to accurately predict the locations of objects undergoing complex and non-linear movements. To tackle these scenarios, we propose a motion-based MOT approach with an enhanced temporal motion predictor, ETTrack. Specifically, the motion predictor integrates a transformer model and a Temporal Convolutional Network (TCN) to capture short-term and long-term motion patterns, and it predicts the future motion of individual objects based on the historical motion information. Additionally, we propose a novel Momentum Correction Loss function that provides additional information regarding the motion direction of objects during training. This allows the motion predictor rapidly adapt to motion variations and more accurately predict future motion. Our experimental results demonstrate that ETTrack achieves a competitive performance compared with state-of-the-art trackers on DanceTrack and SportsMOT, scoring 56.4% and 74.4% in HOTA metrics, respectively.