# DeconfuseTrack:Dealing with Confusion for Multi-Object Tracking arXiv: https://arxiv.org/abs/2403.02767 ยท HTML: https://mlsanity.com/paper/2403.02767/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 48 of its 62 references that are in the map. - First posted on arXiv 5 Mar 2024. ## Builds on (most cited first, 30 of 48) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015) - [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019) - [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (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) - [Tracking without bells and whistles](https://mlsanity.com/paper/1903.05625/) (2019) - [MOTR: End-to-End Multiple-Object Tracking with Transformer](https://mlsanity.com/paper/2105.03247/) (2021) - [MOT20: A benchmark for multi object tracking in crowded scenes](https://mlsanity.com/paper/2003.09003/) (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) - [Quasi-Dense Similarity Learning for Multiple Object Tracking](https://mlsanity.com/paper/2006.06664/) (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) - [FastReID: A Pytorch Toolbox for General Instance Re-identification](https://mlsanity.com/paper/2006.02631/) (2020) - [MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors](https://mlsanity.com/paper/2211.09791/) (2022) - [Deep OC-SORT: Multi-Pedestrian Tracking by Adaptive Re-Identification](https://mlsanity.com/paper/2302.11813/) (2023) - [MeMOT: Multi-Object Tracking with Memory](https://mlsanity.com/paper/2203.16761/) (2022) - [MeMOTR: Long-Term Memory-Augmented Transformer for Multi-Object Tracking](https://mlsanity.com/paper/2307.15700/) (2023) - [Multiple Object Tracking with Correlation Learning](https://mlsanity.com/paper/2104.03541/) (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) - [MotionTrack: Learning Robust Short-term and Long-term Motions for Multi-Object Tracking](https://mlsanity.com/paper/2303.10404/) (2023) ## Built on by (most cited first, 9 of 9) - [Multiple Object Tracking as ID Prediction](https://mlsanity.com/paper/2403.16848/) (2024) - [Omnidirectional Multi-Object Tracking](https://mlsanity.com/paper/2503.04565/) (2025) - [OmniTrack++: Omnidirectional Multi-Object Tracking by Learning Large-FoV Trajectory Feedback](https://mlsanity.com/paper/2511.00510/) (2025) - [Is a Pure Transformer Effective for Separated and Online Multi-Object Tracking?](https://mlsanity.com/paper/2405.14119/) (2024) - [Depth-Aware Scoring and Hierarchical Alignment for Multiple Object Tracking](https://mlsanity.com/paper/2506.00774/) (2025) - [Tracking the Unstable: Appearance-Guided Motion Modeling for Robust Multi-Object Tracking in UAV-Captured Videos](https://mlsanity.com/paper/2508.01730/) (2025) - [Video Individual Counting and Tracking from Moving Drones: A Benchmark and Methods](https://mlsanity.com/paper/2601.12500/) (2026) - [SAMOFT: Robust Multi-Object Tracking via Region and Flow](https://mlsanity.com/paper/2605.09417/) (2026) - [VLA-ReID: Video-Level Association for Re-Identification in Multi-Object Tracking with Highly Similar Objects](https://mlsanity.com/paper/2607.17157/) (2026) ## Abstract (arXiv) Accurate data association is crucial in reducing confusion, such as ID switches and assignment errors, in multi-object tracking (MOT). However, existing advanced methods often overlook the diversity among trajectories and the ambiguity and conflicts present in motion and appearance cues, leading to confusion among detections, trajectories, and associations when performing simple global data association. To address this issue, we propose a simple, versatile, and highly interpretable data association approach called Decomposed Data Association (DDA). DDA decomposes the traditional association problem into multiple sub-problems using a series of non-learning-based modules and selectively addresses the confusion in each sub-problem by incorporating targeted exploitation of new cues. Additionally, we introduce Occlusion-aware Non-Maximum Suppression (ONMS) to retain more occluded detections, thereby increasing opportunities for association with trajectories and indirectly reducing the confusion caused by missed detections. Finally, based on DDA and ONMS, we design a powerful multi-object tracker named DeconfuseTrack, specifically focused on resolving confusion in MOT. Extensive experiments conducted on the MOT17 and MOT20 datasets demonstrate that our proposed DDA and ONMS significantly enhance the performance of several popular trackers. Moreover, DeconfuseTrack achieves state-of-the-art performance on the MOT17 and MOT20 test sets, significantly outperforms the baseline tracker ByteTrack in metrics such as HOTA, IDF1, AssA. This validates that our tracking design effectively reduces confusion caused by simple global association.