## Beyond the Survey: A Systematic Empirical Study of Detection and Association in Visual MOT Official implementation repository for the paper **"Beyond the Survey: A Systematic Empirical Study of Detection and Association in Visual MOT"**, accepted for publication in *Artificial Intelligence Review*. > **Note:** This repository also includes the official Python implementation for: > **"Adaptive Confidence Threshold for ByteTrack in Multi-Object Tracking"** (*ICCAIS 2023*, [arXiv 2312.01650](https://arxiv.org/abs/2312.01650)) at this folder [bytetrack](trackers/bytetrack). --- ### Overview This repository provides tracking implementations for algorithms evaluated in our study: **SORT, DeepSORT, MOTDT, FairMOT, ByteTrack (AdaptByteTrack), OCSORT, DeepOCSORT, and VisualRFS**. We provide CMC (Camera Motion Compensation) for ByteTrack (AdaptByteTrack), VisualRFS, and OC-SORT. Evaluation Scores/Metrics are: - [CLEAR](https://link.springer.com/article/10.1155/2008/246309), [IDF1](https://arxiv.org/pdf/1609.01775), [HOTA](https://arxiv.org/abs/2009.07736) evaluation available at [JonathonLuiten/TrackEval](https://github.com/JonathonLuiten/TrackEval). - Tracking Effort Measure [TEM](https://arxiv.org/abs/2212.08536) ([vpulab/MOT-evaluation](https://github.com/vpulab/MOT-evaluation)). For other evaluated methods, please clone their respective repositories and follow the authors' original execution instructions. --- ### Datasets & Detection Outputs #### Pre-extracted Detections can be downloaded from Hugging Face ([linhmv/VisualMOT](https://huggingface.co/datasets/linhmv/VisualMOT)). We provide extracted detection files with confidence scores $[0, 1]$ in the ([`./dets/`](./dets/)) directory: | Detector | Venue / Source | Paper / Link | |:-------------------------------| :--- | :--- | | POI: `detector_poi` | ECCV 2016 | [arXiv:1610.06136](https://arxiv.org/pdf/1610.06136) | | JDE: `detector_jde` | ECCV 2020 | [arXiv:1909.12605](https://arxiv.org/abs/1909.12605) | | TraDeS: `detector_trades` | CVPR 2021 | [arXiv:2103.08808](https://arxiv.org/abs/2103.08808) | | FairMOT: `detector_fairmot128` | IJCV 2021 | [arXiv:2004.01888](https://arxiv.org/abs/2004.01888) | | GSDT: `detector_gsdt` | ICRA 2021 | [arXiv:2006.13164](https://arxiv.org/abs/2006.13164) | | CSTrack: `detector_cstrack` | TIP 2022 | [arXiv:2010.12138](https://arxiv.org/abs/2010.12138) | | YOLOX: `detector_bytetrack` | ECCV 2022 | [arXiv:2110.06864](https://arxiv.org/abs/2110.06864) | | YOLOv11: `detectors_yolov11` | arXiv 2024 | [arXiv:2410.17725](https://arxiv.org/abs/2410.17725) | - POI: Relies on [ETHZ](https://ieeexplore.ieee.org/document/4587581), [Caltech Pedestrian](https://ieeexplore.ieee.org/abstract/document/5206631), and a self-collected surveillance dataset. - TraDeS: Utilizes a [CrowdHuman](https://arxiv.org/abs/1805.00123) pre-trained model for 2D tracking alongside the [MOTChallenge](https://motchallenge.net/) dataset. - JDE, FairMOT, CSTrack, GSDT: Fine-tuned on the "[Mix of Six](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md)" dataset, which combines [Caltech Pedestrian](https://ieeexplore.ieee.org/abstract/document/5206631), [CityPersons](https://arxiv.org/abs/1702.05693), [ETHZ](https://ieeexplore.ieee.org/document/4587581), [MOTChallenge](https://motchallenge.net/), [CUHK-SYSU](https://arxiv.org/abs/1604.01850), and [PRW](https://arxiv.org/abs/1604.02531). - YOLOX: Trained on a combination of [MOTChallenge](https://motchallenge.net/), [CrowdHuman](https://arxiv.org/abs/1805.00123), [CityPersons](https://arxiv.org/abs/1702.05693), and [ETHZ](https://ieeexplore.ieee.org/document/4587581). - YOLOv11: `YOLO11x`, Only trained on [COCO Detection](https://cocodataset.org/#overview) Dataset #### Evaluation Datasets Dataset Name | Year | Source| | :--- |:---------------------------------------------------------------------------------------------| :--- | | [MOTChallenge](https://motchallenge.net/)| [2016](https://arxiv.org/abs/1603.00831), [2017](https://motchallenge.net/data/MOT17/), [2020](https://arxiv.org/abs/2003.09003) | |[DanceTrack](https://arxiv.org/abs/2111.14690)| CVPR 2022 |[DanceTrack/DanceTrack](https://github.com/DanceTrack/DanceTrack) |[SportsMOT](https://arxiv.org/abs/2304.05170)| ICCV 2023 |[MCG-NJU/SportsMOT](https://github.com/MCG-NJU/SportsMOT) |[CrowdTrack](https://arxiv.org/abs/2507.02479)| arXiv 2025 | [loseevaya/CrowdTrack](https://github.com/loseevaya/CrowdTrack) --- ### Evaluated Tracking Algorithms #### 1. Analytical Data Association *Hand-crafted motion & appearance models* | Method | Ref. Index | Year | Source Code | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| :---: |:------------:| :--- | | **[SORT](https://arxiv.org/abs/1602.00763)** | [23] | ICIP 2016 | [abewley/sort](https://github.com/abewley/sort) | | **[DeepSORT](https://arxiv.org/abs/1703.07402)** | [27] | ICIP 2017 | [nwojke/deep_sort](https://github.com/nwojke/deep_sort) | | **[MOTDT](https://arxiv.org/abs/1809.04427)** | [28] | ICME 2018 | [longcw/MOTDT](https://github.com/longcw/MOTDT) | | **[FairMOT](https://arxiv.org/abs/2004.01888)** | [29] | IJCV 2021 | [ifzhang/FairMOT](https://github.com/ifzhang/FairMOT) | | **[ByteTrack](https://arxiv.org/abs/2110.06864)** | [24] | ECCV 2022 | [FoundationVision/ByteTrack](https://github.com/FoundationVision/ByteTrack) | | **[AdaptByteTrack](https://arxiv.org/abs/2312.01650)** | [75] | ICCAIS 2023 | [linh-gist/AdaptConfByteTrack](https://github.com/linh-gist/AdaptConfByteTrack) | | **[OCSORT](https://arxiv.org/abs/2203.14360)** | [25] | CVPR 2023 | [noahcao/OC_SORT](https://github.com/noahcao/OC_SORT) | | **[DeepOCSORT](https://arxiv.org/abs/2302.11813)** | [30] | ICIP 2023 | [gerardmaggiolino/deep-oc-sort](https://github.com/gerardmaggiolino/deep-oc-sort) | | **[StrongSORT](https://arxiv.org/abs/2202.13514)** | [32] | TMM 2023 | [dyhBUPT/StrongSORT](https://github.com/dyhBUPT/StrongSORT) | | **[VisualRFS](https://arxiv.org/abs/2407.08872)** | [1] | PR 2024 | [linh-gist/VisualRFS](https://github.com/linh-gist/VisualRFS) | | **[HybridSORT](https://arxiv.org/abs/2308.00783)** | [31] | AAAI 2024 | [ymzis69/HybridSORT](https://github.com/ymzis69/HybridSORT) | | **[TrackTrack](https://openaccess.thecvf.com/content/CVPR2025/html/Shim_Focusing_on_Tracks_for_Online_Multi-Object_Tracking_CVPR_2025_paper.html)** | [26] | CVPR 2025 | [kamkyu94/TrackTrack](https://github.com/kamkyu94/TrackTrack) | #### 2. Deep Learning Data Association *Learned feature-based association* | Method | Ref. Index | Year | Source Code | |:-----------------------------------------------------------------------------------------------------| :---: |:-----------:| :--- | | **[SUSHI](https://arxiv.org/abs/2212.03038)** | [33] | CVPR 2023 | [dvl-tum/sushi](https://github.com/dvl-tum/sushi) | | **[LTTrack](https://ieeexplore.ieee.org/abstract/document/10536914)** | [34] | TCSVT 2024 | [linjiaping1/LTTrack](https://github.com/linjiaping1/LTTrack) | | **[LG-MOT](https://arxiv.org/abs/2406.04844)** | [35] | TCSVT 2025 | [weslee88524/lg-mot](https://github.com/weslee88524/lg-mot) | #### 3. End-to-End (E2E) Data Association *Joint detection and association learning* | Method | Ref. Index | Year | Source Code | |:------------------------------------------------------------------------------| :---: |:----------:| :--- | | **[MOTR](https://arxiv.org/pdf/2105.03247)** | [17] | ECCV 2022 | [megvii-research/MOTR](https://github.com/megvii-research/MOTR) | | **[MeMOTR](https://arxiv.org/abs/2307.15700)** | [41] | ICCV 2023 | [mcg-nju/memotr](https://github.com/mcg-nju/memotr) | | **[MOTIP](https://arxiv.org/abs/2403.16848)** | [42] | CVPR 2025 | [MCG-NJU/MOTIP](https://github.com/MCG-NJU/MOTIP) | | **[CO-MOT](https://arxiv.org/abs/2305.12724)** | [43] | ICLR 2025 | [BingfengYan/CO-MOT](https://github.com/BingfengYan/CO-MOT) | | **[SambaMOTR](https://arxiv.org/abs/2410.01806)** | [39] | ICLR 2025 | [mattiasegu/sambamotr](https://github.com/mattiasegu/sambamotr) | ### Usage 1. **Set Up Python Environment** - Create a `conda` Python environment and activate it: ```sh conda create --name virtualenv python==3.8.0 conda activate virtualenv ``` - lone this repository recursively to have pybind11 ```sh git clone --recursive https://github.com/linh-gist/AdaptConfByteTrack.git ``` - Install Packages ```sh numpy==1.23.1 opencv-python==4.9.0.80 loguru==0.7.2 scipy==1.10.1 lap==0.5.12 cython_bbox==0.1.5 matplotlib==3.5.3 filterpy==1.4.5 motmetrics==1.4.0 openpyxl==3.1.5 pycocotools==2.0.7 tabulate==0.9.0 # git clone https://github.com/JonathonLuiten/TrackEval.git # cd TrackEval, python setup.py build develop ``` 2. **Prepare Data** - Datasets: - MOT16, MOT17, MOT20, DanceTrack, SportsMOT, CrowdTrack - You can also run with your custom dataset but need a detector 3. **Run the Tracking Demo** - Change parameters in `make_parser()` in `track.py` such as `use_gmc`, `data_dir` (MOTChallenge GT data) - Run `python track.py` ### Citation If you find this project useful in your research, please consider citing by: ``` @article{van2026beyond, title={Beyond the Survey: A Systematic Empirical Study of Detection and Association in Visual MOT}, author={Linh Van Ma and Juhua Hu and Wei Cheng and Unse Fatima and Moongu Jeon}, booktitle={Artificial Intelligence Review}, year={2026}, publisher={Springer} } @inproceedings{van2023adaptive, title={Adaptive Confidence Threshold for ByteTrack in Multi-Object Tracking}, author={Linh Van Ma and Muhammad Ishfaq Hussain and JongHyun Park and Jeongbae Kim and Moongu Jeon}, booktitle={2023 12th International Conference on Control, Automation and Information Sciences (ICCAIS)}, pages={370--374}, year={2023}, organization={IEEE} } ``` ### Acknowledgement A part of the code is borrowed from [SORT](https://github.com/abewley/sort), [DeepSORT](https://github.com/nwojke/deep_sort), [MOTDT](https://arxiv.org/abs/1809.04427), [FairMOT](https://arxiv.org/abs/2004.01888), [ByteTrack](https://arxiv.org/abs/2110.06864), [OCSORT](https://arxiv.org/abs/2203.14360), [DeepOCSORT](https://arxiv.org/abs/2302.11813), and [VisualRFS](https://arxiv.org/abs/2407.08872). Thanks for their wonderful works.