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Accurately locating each head's position in the crowd scenes is a crucial task in the field of crowd analysis. However, traditional density-based methods only predict coarse prediction, and segmentation/detection-based methods cannot handle extremely dense scenes and large-range scale-variations crowds.

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SHAHFAISAL80/Crowd-localization-and-counting

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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. framework

Progress

  • Testing Code (2020.12.10)
  • Training Code
    • NWPU (2020.12.14)
    • JHU (2021.01.05)
    • UCF-QNRF (2020.12.30)
    • ShanghaiTech Part A/B (2020.12.29)
    • FDST (2020.12.30)
  • scale information for UCF-QNRF and ShanghaiTech Part A/B (2021.01.07)

Getting Started

Preparation

  • Prerequisites

    • Python 3.7
    • Pytorch 1.6: http://pytorch.org .
    • other libs in requirements.txt, run pip install -r requirements.txt.
  • Code

  • Datasets

    • 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.

    • If you want to reproduce the results on Shanghai Tech Part A/B , UCF-QNRF, and JHU datasets, you can follow the instructions in DATA.md to setup the datasets.

    • 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: val_curve
  • The sub images are the input image, GT, prediction map,localization result, and pixel-level threshold, respectively: val_curve

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

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. under the sigma_l) and pre-trained models on NWPU val set, UCF-QNRF, SHT A, SHT B, and FDST:

Method NWPU val UCF-QNRF SHT A
Paper: VGG+FPN [2,3] 77.0/80.2/74.1 68.8/78.2/61.5 72.5/72.6/72.5
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
Paper: HRNet [1] 80.2/84.1/76.6 72.0/79.3/65.9 73.9/79.8/68.7
This Repo's Reproduction: HRNet [1] 79.8/83.4/76.5 72.0/78.7/66.4 76.1/79.1/73.3
Method SHT B FDST JHU
Paper: VGG+FPN [2,3] 80.2/84.9/76.0 93.1/92.7/93.5 -
This Repo's Reproduction: VGG+FPN [2,3] 81.7/88.5/75.9 93.9/94.7/93.1 61.8/73.2/53.5
Paper: HRNet [1] 86.2/90.7/82.1 95.5/95.3/95.8 62.5/74.0/54.2
This Repo's Reproduction: HRNet [1] 86.0/91.5/81.0 95.7/96.9 /94.4 64.0/73.3/56.8

References

  1. Deep High-Resolution Representation Learning for Visual Recognition, T-PAMI, 2019.
  2. Very Deep Convolutional Networks for Large-scale Image Recognition, arXiv, 2014.
  3. 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).

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.

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Accurately locating each head's position in the crowd scenes is a crucial task in the field of crowd analysis. However, traditional density-based methods only predict coarse prediction, and segmentation/detection-based methods cannot handle extremely dense scenes and large-range scale-variations crowds.

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