Point-Query Quadtree for Crowd Counting, Localization, and More (ICCV 2023)

This repository includes the official implementation of the paper:

Point-Query Quadtree for Crowd Counting, Localization, and More

International Conference on Computer Vision (ICCV), 2023

Chengxin Liu¹, Hao Lu¹, Zhiguo Cao¹, Tongliang Liu²

¹Huazhong University of Science and Technology, China

²The University of Sydney, Australia

[Paper] | [Supplementary]

Highlights

We formulate crowd counting as a decomposable point querying process, where sparse input points could split into four new points when necessary. This formulation exhibits many appealing properties:

• Intuitive: The input and output are both interpretable and steerable

• Generic: PET is applicable to a number of crowd-related tasks, by simply adjusting the input format

• Effective: PET reports state-of-the-art crowd counting and localization results

Installation

• Required packages:

torch
torchvision
numpy
opencv-python
scipy
matplotlib

• Install packages:

pip install -r requirements.txt

Data Preparation

• Download crowd-counting datasets, e.g., ShanghaiTech.

• We expect the directory structure to be as follows:

PET
├── data
│    ├── ShanghaiTech
├── datasets
├── models
├── ...

• Alternatively, you can define the path of the dataset in datasets/init.py

• For UCF-QNRF, JHU-Crowd++, and NWPU-Crowd datasets, please refer to preprocess_dataset.py:

  • change dataset and data_root
  • run python preprocess_dataset.py

Training

• Download ImageNet pretrained vgg16_bn, and put it in pretrained folder. Or you can define your pre-trained model path in models/backbones/vgg.py

• To train PET on ShanghaiTech PartA, run

  sh train.sh
  

Evaluation

• Modify eval.sh
  • change --resume to your local model path
• Run

sh eval.sh

Pretrained Models

• Environment:

python==3.8
pytorch==1.12.1
torchvision==0.13.1

• Models:

Dataset	Model Link	Training Log	MAE
ShanghaiTech PartA	SHA_model.pth	SHA_log.txt	49.08
ShanghaiTech PartB	SHB_model.pth	SHB_log.txt	6.18
UCF_QNRF	UCF_QNRF.pth	-	-
JHU_Crowd	JHU_Crowd.pth	-	-
NWPU_Crowd	NWPU_Crowd.pth	-	-

Citation

If you find this work helpful for your research, please consider citing:

@InProceedings{liu2023pet,
  title={Point-Query Quadtree for Crowd Counting, Localization, and More},
  author={Liu, Chengxin and Lu, Hao and Cao, Zhiguo and Liu, Tongliang},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2023}
}

Permission

This code is for academic purposes only. Contact: Chengxin Liu ([email protected])

Acknowledgement

We thank the authors of DETR and P2PNet for open-sourcing their work.