IIM - Crowd Localization

This repo is the official implementation of paper: Learning Independent Instance Maps for Crowd Localization. The code is developed based on C3F.

Progress

For JHU-CROWD, we will upload the processed data and then provide their training details/pre-trained models, of which the key parameters are the same as NWPU experiments.

Getting Started

Preparation

Prerequisites
- Python 3.7
- Pytorch 1.6: http://pytorch.org .
- other libs in requirements.txt, run pip install -r requirements.txt.
Preparation
- Clone this repo in the directory (Root/IIM):
- Download NWPU-Crowd dataset from this link.
- Unzip *zip files in turns and place images_part* into the same folder (Root/ProcessedData/NWPU/images).
- Download the processing labels and val gt file from this link. Place them into Root/ProcessedData/NWPU/masks and Root/ProcessedData/NWPU, respectively.
- Download the pre-trained HR models from this link. More details are availble at HRNet-Semantic-Segmentation and HRNet-Image-Classification.
Finally, the folder tree is below:

   -- ProcessedData
   	|-- NWPU
   		|-- images
   		|   |-- 0001.jpg
   		|   |-- 0002.jpg
   		|   |-- ...
   		|   |-- 5109.jpg
   		|-- masks
   		|   |-- 0001.png
   		|   |-- 0002.png
   		|   |-- ...
   		|   |-- 3609.png
   		|-- train.txt
   		|-- val.txt
   		|-- test.txt
   		|-- val_gt_loc.txt
   -- PretrainedModels
     |-- hrnetv2_w48_imagenet_pretrained.pth
   -- IIM
     |-- datasets
     |-- misc
     |-- ...

Training

run python train.py.
run tensorboard --logdir=exp --port=6006.
The validtion records are shown as follows:
The sub images are the input image, GT, prediction map,localization result, and pixel-level threshold, respectively:

Tips: The training process takes ~50 hours on NWPU datasets with two TITAN RTX (48GB Memeory).

Testing and Submitting

Modify some key parameters in test.py:
- netName.
- model_path.
Run python test.py. Then the output file (*_*_test.txt) will be generated, which can be directly submitted to CrowdBenchmark

Visualization on the val set

Modify some key parameters in test.py:
- test_list = 'val.txt'
- netName.
- model_path.
Run python test.py. Then the output file (*_*_val.txt) will be generated.
Modify some key parameters in vis4val.py:
- pred_file.
Run python vis4val.py.

Performance

The results (F1, Pre., Rec.) and pre-trained models on NWPU val set, UCF-QNRF, SHT A, SHT B, and FDST:

Method	NWPU val	UCF-QNRF	SHT A	SHT B	FDST
Paper: VGG+FPN [2,3]	77.0/80.2/74.1	68.8/78.2/61.5	72.5/72.6/72.5	80.2/84.9/76.0	93.1/92.7/93.5
This Repo's Reproduction: VGG+FPN [2,3]	77.1/82.5/72.3	67.8/75.7/61.5	71.6/75.9/67.8	81.7/88.5/75.9	93.9/94.7/93.1
Paper: HRNet [1]	80.2/84.1/76.6	72.0/79.3/65.9	73.9/79.8/68.7	86.2/90.7/82.1	95.5/95.3/95.8
This Repo's Reproduction: HRNet [1]	79.8/83.4/76.5	72.0/78.7/66.4	76.1/79.1/73.3	86.0/91.5/81.0	95.7/96.9 /94.4

References

Deep High-Resolution Representation Learning for Visual Recognition, T-PAMI, 2019.
Very Deep Convolutional Networks for Large-scale Image Recognition, arXiv, 2014.
Feature Pyramid Networks for Object Detection, CVPR, 2017.

About the leaderboard on the test set, please visit Crowd benchmark. Our submissions are the IIM(HRNet) and IIM (VGG16).

Video Demo

We test the pretrained HR Net model on the NWPU dataset in a real-world subway scene. Please visit bilibili or YouTube to watch the video demonstration.

Citation

If you find this project is useful for your research, please cite:

@article{gao2020learning,
  title={Learning Independent Instance Maps for Crowd Localization},
  author={Gao, Junyu and Han, Tao and Yuan, Yuan and Wang, Qi},
  journal={arXiv preprint arXiv:2012.04164},
  year={2020}
}

Our code borrows a lot from the C^3 Framework, and you may cite:

@article{gao2019c,
  title={C$^3$ Framework: An Open-source PyTorch Code for Crowd Counting},
  author={Gao, Junyu and Lin, Wei and Zhao, Bin and Wang, Dong and Gao, Chenyu and Wen, Jun},
  journal={arXiv preprint arXiv:1907.02724},
  year={2019}
}

If you use pre-trained models in this repo (HR Net, VGG, and FPN), please cite them.

sasakisasaki / IIM