DocumentAIKit/configs/mot/mcfairmot

English | 简体中文

MCFairMOT (Multi-class FairMOT)

Table of Contents

• Introduction
• Model Zoo
• Getting Start
• Citations

Introduction

MCFairMOT is the Multi-class extended version of FairMOT.

PP-Tracking real-time MOT system

In addition, PaddleDetection also provides PP-Tracking real-time multi-object tracking system. PP-Tracking is the first open source real-time Multi-Object Tracking system, and it is based on PaddlePaddle deep learning framework. It has rich models, wide application and high efficiency deployment.

PP-Tracking supports two paradigms: single camera tracking (MOT) and multi-camera tracking (MTMCT). Aiming at the difficulties and pain points of actual business, PP-Tracking provides various MOT functions and applications such as pedestrian tracking, vehicle tracking, multi-class tracking, small object tracking, traffic statistics and multi-camera tracking. The deployment method supports API and GUI visual interface, and the deployment language supports Python and C++, The deployment platform environment supports Linux, NVIDIA Jetson, etc.

AI studio public project tutorial

PP-tracking provides an AI studio public project tutorial. Please refer to this tutorial.

Model Zoo

MCFairMOT Results on VisDrone2019 Val Set

backbone	input shape	MOTA	IDF1	IDS	FPS	download	config
DLA-34	1088x608	24.3	41.6	2314	-	model	config
HRNetV2-W18	1088x608	20.4	39.9	2603	-	model	config
HRNetV2-W18	864x480	18.2	38.7	2416	-	model	config
HRNetV2-W18	576x320	12.0	33.8	2178	-	model	config

Notes:

• MOTA is the average MOTA of 10 catecories in the VisDrone2019 MOT dataset, and its value is also equal to the average MOTA of all the evaluated video sequences. Here we provide the download link of the dataset.
• MCFairMOT used 4 GPUs for training 30 epoches. The batch size is 6 on each GPU for MCFairMOT DLA-34, and 8 for MCFairMOT HRNetV2-W18.

MCFairMOT Results on VisDrone Vehicle Val Set

backbone	input shape	MOTA	IDF1	IDS	FPS	download	config
DLA-34	1088x608	37.7	56.8	199	-	model	config
HRNetV2-W18	1088x608	35.6	56.3	190	-	model	config

Notes:

• MOTA is the average MOTA of 4 catecories in the VisDrone Vehicle dataset, and this dataset is extracted from the VisDrone2019 MOT dataset, here we provide the download link.
• The tracker used in MCFairMOT model here is ByteTracker.

MCFairMOT off-line quantization results on VisDrone Vehicle val-set

Model	Compression Strategy	Prediction Delay（T4）	Prediction Delay（V100）	Model Configuration File	Compression Algorithm Configuration File
DLA-34	baseline	41.3	21.9	Configuration File	-
DLA-34	off-line quantization	37.8	21.2	Configuration File	Configuration File

Getting Start

1. Training

Training MCFairMOT on 4 GPUs with following command

python -m paddle.distributed.launch --log_dir=./mcfairmot_dla34_30e_1088x608_visdrone/ --gpus 0,1,2,3 tools/train.py -c configs/mot/mcfairmot/mcfairmot_dla34_30e_1088x608_visdrone.yml

2. Evaluation

Evaluating the track performance of MCFairMOT on val dataset in single GPU with following commands:

# use weights released in PaddleDetection model zoo
CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/mcfairmot/mcfairmot_dla34_30e_1088x608_visdrone.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/mcfairmot_dla34_30e_1088x608_visdrone.pdparams

# use saved checkpoint in training
CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/mcfairmot/mcfairmot_dla34_30e_1088x608_visdrone.yml -o weights=output/mcfairmot_dla34_30e_1088x608_visdrone/model_final.pdparams

Notes:

• The default evaluation dataset is VisDrone2019 MOT val-set. If you want to change the evaluation dataset, please refer to the following code and modify configs/datasets/mcmot.yml： EvalMOTDataset: !MOTImageFolder dataset_dir: dataset/mot data_root: your_dataset/images/val keep_ori_im: False # set True if save visualization images or video
• Tracking results will be saved in {output_dir}/mot_results/, and every sequence has one txt file, each line of the txt file is frame,id,x1,y1,w,h,score,cls_id,-1,-1, and you can set {output_dir} by --output_dir.

3. Inference

Inference a video on single GPU with following command:

# inference on video and save a video
CUDA_VISIBLE_DEVICES=0 python tools/infer_mot.py -c configs/mot/mcfairmot/mcfairmot_dla34_30e_1088x608_visdrone.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/mcfairmot_dla34_30e_1088x608_visdrone.pdparams --video_file={your video name}.mp4  --save_videos

Notes:

• Please make sure that ffmpeg is installed first, on Linux(Ubuntu) platform you can directly install it by the following command:apt-get update && apt-get install -y ffmpeg.

4. Export model

CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/mcfairmot/mcfairmot_dla34_30e_1088x608_visdrone.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/mcfairmot_dla34_30e_1088x608_visdrone.pdparams

5. Using exported model for python inference

python deploy/pptracking/python/mot_jde_infer.py --model_dir=output_inference/mcfairmot_dla34_30e_1088x608_visdrone --video_file={your video name}.mp4 --device=GPU --save_mot_txts

Notes:

• The tracking model is used to predict the video, and does not support the prediction of a single image. The visualization video of the tracking results is saved by default. You can add --save_mot_txts to save the txt result file, or --save_images to save the visualization images.
• Each line of the tracking results txt file is frame,id,x1,y1,w,h,score,cls_id,-1,-1.

6. Off-line quantization

The offline quantization model is calibrated using the VisDrone Vehicle val-set, running as:

CUDA_VISIBLE_DEVICES=0 python3.7 tools/post_quant.py -c configs/mot/mcfairmot/mcfairmot_dla34_30e_1088x608_visdrone_vehicle_bytetracker.yml --slim_config=configs/slim/post_quant/mcfairmot_ptq.yml

Notes:

• Offline quantization uses the VisDrone Vehicle val-set dataset and a 4-class vehicle tracking model by default.

Citations

@article{zhang2020fair,
  title={FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking},
  author={Zhang, Yifu and Wang, Chunyu and Wang, Xinggang and Zeng, Wenjun and Liu, Wenyu},
  journal={arXiv preprint arXiv:2004.01888},
  year={2020}
}

@ARTICLE{9573394,
  author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  title={Detection and Tracking Meet Drones Challenge},
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TPAMI.2021.3119563}
}

@article{zhang2021bytetrack,
  title={ByteTrack: Multi-Object Tracking by Associating Every Detection Box},
  author={Zhang, Yifu and Sun, Peize and Jiang, Yi and Yu, Dongdong and Yuan, Zehuan and Luo, Ping and Liu, Wenyu and Wang, Xinggang},
  journal={arXiv preprint arXiv:2110.06864},
  year={2021}
}