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FairMOT (FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking)

Table of Contents

Introduction

FairMOT is based on an Anchor Free detector Centernet, which overcomes the problem of anchor and feature misalignment in anchor based detection framework. The fusion of deep and shallow features enables the detection and ReID tasks to obtain the required features respectively. It also uses low dimensional ReID features. FairMOT is a simple baseline composed of two homogeneous branches propose to predict the pixel level target score and ReID features. It achieves the fairness between the two tasks and obtains a higher level of real-time MOT performance.

Model Zoo

FairMOT Results on MOT-16 Training Set

backbone input shape MOTA IDF1 IDS FP FN FPS download config
DLA-34(paper) 1088x608 83.3 81.9 544 3822 14095 - - -
DLA-34 1088x608 83.2 83.1 499 3861 14223 - model config

FairMOT Results on MOT-16 Test Set

backbone input shape MOTA IDF1 IDS FP FN FPS download config
DLA-34(paper) 1088x608 74.9 72.8 1074 - - 25.9 - -
DLA-34 1088x608 75.0 74.7 919 7934 36747 - model config

Notes: FairMOT used 2 GPUs for training and mini-batch size as 6 on each GPU, and trained for 30 epoches.

Getting Start

1. Training

Training FairMOT on 2 GPUs with following command

python -m paddle.distributed.launch --log_dir=./fairmot_dla34_30e_1088x608/ --gpus 0,1 tools/train.py -c configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml

2. Evaluation

Evaluating the track performance of FairMOT 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/fairmot/fairmot_dla34_30e_1088x608.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams

# use saved checkpoint in training
CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml -o weights=output/fairmot_dla34_30e_1088x608/model_final.pdparams

Notes: The default evaluation dataset is MOT-16 Train Set. If you want to change the evaluation dataset, please refer to the following code and modify configs/datasets/mot.yml：

EvalMOTDataset:
  !MOTImageFolder
    dataset_dir: dataset/mot
    data_root: MOT17/images/train
    keep_ori_im: False # set True if save visualization images or video

3. Inference

Inference a vidoe 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/fairmot/fairmot_dla34_30e_1088x608.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.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/fairmot/fairmot_dla34_30e_1088x608.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams

5. Using exported model for python inference

python deploy/python/mot_jde_infer.py --model_dir=output_inference/fairmot_dla34_30e_1088x608 --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.

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}
}