PFTrack

Visible-Thermal Multiple Object Tracking: Large-scale Video Dataset and Progressive Fusion Approach

Visible-Thermal Multiple Object Tracking: Large-scale Video Dataset and Progressive Fusion Approach,
Yabin Zhu, Qianwu Wang, Chenglong Li, Jin Tang, Zhixiang Huang

@misc{zhu2024visiblethermalmultipleobjecttracking,
  title={Visible-Thermal Multiple Object Tracking: Large-scale Video Dataset and Progressive Fusion Approach}, 
  author={Yabin Zhu and Qianwu Wang and Chenglong Li and Jin Tang and Zhixiang Huang},
  year={2024},
  eprint={2408.00969},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2408.00969}, 

} Contact: [email protected]. Any questions or discussion are welcome!

Abstract

The complementary benefits from visible and thermal infrared data are widely utilized in various computer vision task, such as visual tracking, semantic segmentation and object detection, but rarely explored in Multiple Object Tracking (MOT). In this work, we contribute a large-scale Visible-Thermal video benchmark for MOT, called VT-MOT. VT-MOT has the following main advantages. 1) The data is large scale and high diversity. VT-MOT includes 582 video sequence pairs, 401k frame pairs from surveillance, drone, and handheld platforms. 2) The cross-modal alignment is highly accurate. We invite several professionals to perform both spatial and temporal alignment frame by frame. 3) The annotation is dense and high-quality. VT-MOT has 3.99 million annotation boxes annotated and double-checked by professionals, including heavy occlusion and object re-acquisition (object disappear and reappear) challenges. To provide a strong baseline, we design a simple yet effective tracking framework, which effectively fuses temporal information and complementary information of two modalities in a progressive manner, for robust visible-thermal MOT. A comprehensive experiment are conducted on VT-MOT and the results prove the superiority and effectiveness of the proposed method compared with state-of-the-art methods.

contribution

• We build a large-scale visible-thermal MOT dataset VT-MOT, which can promote the research and development of MOT in all weather and all day. The dataset includes 582 video sequence pairs with 401k frame pairs captured in surveillance, drone and handheld platforms.
• We perform manual spatio-temporal alignment of all video sequences of both modalities in a frame by frame manner to ensure high-quality alignment of the two modalities. Moreover, dense and high-quality annotation is provided for comprehensive evaluation of different MOT algorithms. These annotation contain 3.99 million bounding boxes and heavy occlusion and object re-acquisition challenge labels.
• We also propose a simple yet effective progressive fusion tracking framework, which effectively fuses temporal and complementary information of two modalities in a progressive manner, for robust visible-thermal MOT.
• We perform numerous experiments on VT-MOT dataset, and the results prove the superiority and effectiveness of the proposed method compared with state-of-the-art methods.

Main results

Tracking performance comparison of several evaluated trackers on VT-MOT testing set.

Method	Publication	Modality	HOTA	DetA	MOTP	IDF1	MOTA
FairMOT	IJCV 2021	V-T	37.35	34.628	72.525	45.795	37.266
CenterTrack	ECCV 2020	V-T	39.045	38.104	72.874	44.42	30.585
TraDes	CVPR 2021	V-T	38.319	36.37	72.295	47.008	34.632
TransTrack	arXiv 2021	V-T	38.00	35.711	73.823	43.567	36.156
ByteTracK	ECCV 2022	V-T	38.393	32.122	73.483	45.757	33.151
OC-SORT	CVPR 2023	V-T	31.479	25.244	73.15	38.086	28.948
MixSort-OC	ICCV 2023	V-T	39.09	33.109	73.632	45.799	31.33
MixSort-Byte	ICCV 2023	V-T	39.575	34.806	73.049	46.367	31.593
PID-MOT	TCSVT 2023	V-T	35.621	33.245	71.794	42.43	33.333
Hybrid-SORT	AAAI 2024	V-T	39.485	34.619	72.840	46.310	31.074
**Ours **	-	V-T	41.068	41.631	73.949	47.254	43.088

our model can be got in [model](https://pan.baidu.com/s/17ykng7S9x8wVlULeucKryQ?pwd=233w [233w])

Installation

Please refer to INSTALL.md for installation instructions.

Dataset preparation

If you want to reproduce the results in the paper for benchmark evaluation or training, you will need to setup datasets.

VT-MOT

MOT is used to train and evaluate the system. We will only use the training set and testing set for developing this project.

We have packed the dataset preprocessing code as a script.

The script includes:

• Download and unzip the dataset from [VTMOT](https://pan.baidu.com/s/1C8rXxVmxg6jAB7Xs7E45zw?pwd=chcw [chcw]).

• Convert it into COCO format using tools/convert_mot_rgbt_to_coco.py.

• The output data structure should be:

  ${PFTrack_ROOT}
  |-- data
  `-- |-- VTMOT
      `-- |--- train
          |   |--- video1
          |   |    |--- visible
          |	|	 |	 |---0000001.jpg
          |	|	 |	 |---0000002.jpg
          |	|	 |	 |---...
          |   |	 |--- infrared
          |	|	 |	 |---0000001.jpg
          |	|	 |	 |---0000002.jpg
          |	|	 |	 |---...
          |   |    |--- gt
          |   |    |   |--- gt.txt
          |   |    |--- seqinfo
          |   |--- video2
          |   |--- ...
          |--- test
          |   |--- video1
          |   |    |--- visible
          |	|	 |	 |---0000001.jpg
          |	|	 |	 |---0000002.jpg
          |	|	 |	 |---...
          |   |	 |--- infrared
          |	|	 |	 |---0000001.jpg
          |	|	 |	 |---0000002.jpg
          |	|	 |	 |---...
          |   |    |--- gt
          |   |    |   |--- gt.txt
          |   |    |--- seqinfo
          |   |--- video2
          |   |--- ...
          `---| annotations
              |--- train.json
              `--- test.json
  

Use PFTrack

Training on VT-MOT

python -u main.py tracking --modal RGB-T --save_all  --exp_id VTMOT_PFTrack  --dataset mot_rgbt --dataset_version mot_rgbt --load_model "./exp/tracking/VTMOT_RGBT/***.pth" --batch_size 12 --pre_hm --ltrb_amodal --same_aug --hm_disturb 0.05 --lost_disturb 0.4 --fp_disturb 0.1 --gpus 0 

Testing on VT-MOT

python test_rgbt.py tracking --modal RGB-T --test_mot_rgbt True --exp_id VTMOT_PFTrack --dataset mot_rgbt --dataset_version mot_rgbt --pre_hm --ltrb_amodal --track_thresh 0.4 --pre_thresh 0.5 --load_model ./exp/tracking/VTMOT_RGBT/model.pth

Eval

cd trackeval
python run_mot_challenge.py

Acknowledgement

Tracking Objects as Points: https://github.com/xingyizhou/CenterTrack

trackeval: https://github.com/JonathonLuiten/TrackEval