You can deploy this model and start earning money today!

UHow/
LG-MOT

Views

No views yet

LG-MOT

Multi-Granularity Language-Guided Training for Multi-Object Tracking

Yuhao Li, Jiale Cao, Muzammal Naseer, Yu Zhu, Jinqiu Sun, Yanning Zhang and Fahad Khan

Northwestern Polytechnical University， Mohamed bin Zayed University of AI, TianJin University, Khalifa University, Linköping University

paper

Latest

2025/08/15: We released our annotated DanceTrack-L, MOT17-L, and SportsMOT-L.
2025/05/22: Our work has been accepted by IEEE TCSVT.
2024/06/14: We released our trained models.
2024/06/12: We released our code on github.
2024/06/11: We released our technical report on arxiv. Our code and models are coming soon!

Abstract

Most existing multi-object tracking methods typically learn visual tracking features via maximizing dis-similarities of different instances and minimizing similarities of the same instance. While such a feature learning scheme achieves promising performance, learning discriminative features solely based on visual information is challenging especially in case of environmental interference such as occlusion, blur and domain variance. In this work, we argue that multi-modal language-driven features provide complementary information to classical visual features, thereby aiding in improving the robustness to such environmental interference. To this end, we propose a new multi-object tracking framework, named LG-MOT, that explicitly leverages language information at different levels of granularity (scene-and instance-level) and combines it with standard visual features to obtain discriminative representations. To develop LG-MOT, we annotate existing MOT datasets with scene-and instance-level language descriptions. We then encode both instance-and scene-level language information into high-dimensional embeddings, which are utilized to guide the visual features during training. At inference, our LG-MOT uses the standard visual features without relying on annotated language descriptions. Extensive experiments on three benchmarks, MOT17, DanceTrack and SportsMOT, reveal the merits of the proposed contributions leading to state-of-the-art performance. On the DanceTrack test set, our LG-MOT achieves an absolute gain of 2.2% in terms of target object association (IDF1 score), compared to the baseline using only visual features. Further, our LG-MOT exhibits strong cross-domain generalizability.

Intro

LG-MOT first annotate the training sets and validation sets of commonly used MOT datasets including MOT17, DanceTrack and SportsMOT with language descriptions at both scene and instance levels.

Dataset	Videos (Scenes)	Annotated Scenes	Tracks (Instances)	Annotated Instances	Annotated Boxes	Frames
MOT17-L	7	7	796	796	614,103	110,407
DanceTrack-L	65	65	682	682	576,078	67,304
SportsMOT-L	90	90	1,280	1,280	608,152	55,544
Total	162	162	2,758	2,758	1798,333	233,255

LG-MOT is a new multi-object tracking framework which leverages language information at different granularity during training to enhance object association capabilities. During training, our ISG module aligns each node embedding $\phi(b_i^k)$ with instance-level descriptions embeddings $\varphi_i$, while our SPG module aligns edge embeddings $\hat{E}_{(u,v)}$ with scene-level descriptions embeddings $\varphi_s$ to guide correlation estimation after message passing. Our approach does not require language description during inference.
LG-MOT increases 1.2% in terms of IDF1 over the baseline SUSHI intra-domain, while significantly improves 11.2% in cross-domain.

Performance on Benchmarks

MOT17 Challenge - Test Set

Dataset	IDF1	HOTA	MOTA	ID Sw.	model
MOT17	81.7	65.6	81.0	1161	checkpoint.pth

DanceTrack - Test Set

Dataset	IDF1	HOTA	MOTA	AssA	DetA	model
DanceTrack	60.5	61.8	89.0	80.0	47.8	checkpoint.pth

SportsMOT - Test Set

Dataset	IDF1	HOTA	MOTA	ID Sw.	model
SportsMOT	77.1	75.0	91.0	2847	checkpoint.pth

Setup

Clone and enter this repository

git clone https://github.com/WesLee88524/LG-MOT.git
cd LG-MOT

Create an Anaconda environment for this project:

conda env create -f environment.yml
conda activate LG-MOT

Clone fast-reid (latest version should be compatible but we use this version) and install its dependencies. The fast-reid repo should be inside LG-MOT root:
```
LG-MOT
├── src
├── fast-reid
└── ...
```
Download re-identification model we use from fast-reid and move it inside LG-MOT/fastreid-models/model_weights/

Download MOT17, SPORTSMOT and DanceTrack datasets. In addition, prepare seqmaps to run evaluation (for details see TrackEval). We provide an example seqmap. Overall, the expected folder structure is:

DATA_PATH
├── DANCETRACK
│   └── ...
├── SPORTSMOT
│   └── ...
└── MOT17
    └── seqmaps
    │    ├── seqmap_file_name_matching_split_name.txt
    │    └── ...
    └── train
    │    ├── MOT17-02
    │    │   ├── det
    │    │   │   └── det.txt
    │    │   └── gt
    │    │   │   └── gt.txt
    │    │   └── img1 
    │    │   │   └── ...
    │    │   └── seqinfo.ini
    │    └── ...
    └── test
         └── ...

Training

You can launch a training from the command line. An example command for MOT17 training:

RUN=example_mot17_training
REID_ARCH='fastreid_msmt_BOT_R50_ibn'

DATA_PATH=YOUR_DATA_PATH

python scripts/main.py --experiment_mode train --cuda --train_splits MOT17-train-all --val_splits MOT17-train-all --run_id ${RUN}_${REID_ARCH} --interpolate_motion --linear_center_only --det_file byte065 --data_path ${DATA_PATH} --reid_embeddings_dir reid_${REID_ARCH} --node_embeddings_dir node_${REID_ARCH}  --zero_nodes --reid_arch $REID_ARCH --edge_level_embed --save_cp --inst_KL_loss 1 --KL_loss 1 --num_epoch 200 --mpn_use_prompt_edge 1 1 1 1 --use_instance_prompt

Testing

You can test a trained model from the command line. An example for testing on MOT17 training set:

RUN=example_mot17_test
REID_ARCH='fastreid_msmt_BOT_R50_ibn'

DATA_PATH=your_data_path
PRETRAINED_MODEL_PATH=your_pretrained_model_path

python scripts/main.py --experiment_mode test --cuda --test_splits MOT17-test-all --run_id ${RUN}_${REID_ARCH} --interpolate_motion --linear_center_only --det_file byte065 --data_path ${DATA_PATH} --reid_embeddings_dir reid_${REID_ARCH} --node_embeddings_dir node_${REID_ARCH}  --zero_nodes --reid_arch $REID_ARCH --edge_level_embed --save_cp --hicl_model_path ${PRETRAINED_MODEL_PATH}  --inst_KL_loss 1 --KL_loss 1 --mpn_use_prompt_edge 1 1 1 1

Citation

if you use our work, please consider citing us:

BibTeX

@ARTICLE{11010839,
  author={Li, Yuhao and Cao, Jiale and Naseer, Muzammal and Zhu, Yu and Sun, Jinqiu and Zhang, Yanning and Khan, Fahad Shahbaz},
  journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
  title={Multi-Granularity Language-Guided Training for Multi-Object Tracking}, 
  year={2025},
  volume={},
  number={},
  pages={1-1},
  keywords={Visualization;Training;Feature extraction;Standards;Object detection;Accuracy;Representation learning;Current transformers;Circuits and systems;Target tracking;Multi-object tracking;Language-guided features;Cross-domain generalizability},
  doi={10.1109/TCSVT.2025.3572810}}

License

This project is released under the Apache license. See LICENSE for additional details.

Acknowledgement

The code is mainly based on SUSHI. Thanks for their wonderful works.

UHow/LG-MOT