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JDE (Towards Real-Time Multi-Object Tracking)

Table of Contents

Introduction

  • JDE (Joint Detection and Embedding) learns the object detection task and appearance embedding task simutaneously in a shared neural network. And the detection results and the corresponding embeddings are also outputed at the same time. JDE original paper is based on an Anchor Base detector YOLOv3, adding a new ReID branch to learn embeddings. The training process is constructed as a multi-task learning problem, taking into account both accuracy and speed.

Model Zoo

JDE Results on MOT-16 Training Set

backbone input shape MOTA IDF1 IDS FP FN FPS download config
DarkNet53 1088x608 72.0 66.9 1397 7274 22209 - model config
DarkNet53 864x480 69.1 64.7 1539 7544 25046 - model config
DarkNet53 576x320 63.7 64.4 1310 6782 31964 - model config

JDE Results on MOT-16 Test Set

backbone input shape MOTA IDF1 IDS FP FN FPS download config
DarkNet53(paper) 1088x608 64.4 55.8 1544 - - - - -
DarkNet53 1088x608 64.6 58.5 1864 10550 52088 - model config
DarkNet53(paper) 864x480 62.1 56.9 1608 - - - - -
DarkNet53 864x480 63.2 57.7 1966 10070 55081 - model config
DarkNet53 576x320 59.1 56.4 1911 10923 61789 - model config

Notes: JDE used 8 GPUs for training and mini-batch size as 4 on each GPU, and trained for 30 epoches.

Getting Start

1. Training

Training JDE on 8 GPUs with following command

python -m paddle.distributed.launch --log_dir=./jde_darknet53_30e_1088x608/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/mot/jde/jde_darknet53_30e_1088x608.yml

2. Evaluation

Evaluating the track performance of JDE 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/jde/jde_darknet53_30e_1088x608.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams

# use saved checkpoint in training
CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/jde/jde_darknet53_30e_1088x608.yml -o weights=output/jde_darknet53_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

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,-1,-1,-1, and you can set {output_dir} by --output_dir.

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/jde/jde_darknet53_30e_1088x608.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_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/jde/jde_darknet53_30e_1088x608.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams

5. Using exported model for python inference

python deploy/pptracking/python/mot_jde_infer.py --model_dir=output_inference/jde_darknet53_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. Each line of the tracking results txt file is frame,id,x1,y1,w,h,score,-1,-1,-1.

Citations

@article{wang2019towards,
  title={Towards Real-Time Multi-Object Tracking},
  author={Wang, Zhongdao and Zheng, Liang and Liu, Yixuan and Wang, Shengjin},
  journal={arXiv preprint arXiv:1909.12605},
  year={2019}
}