surveillance_monitoring/dependence/PaddleDetection/configs/mot/jde

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JDE (Towards Real-Time Multi-Object Tracking)

Table of Contents

• Introduction
• Model Zoo
• Getting Start
• Citations

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.

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

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}
}