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S-DCNet

This is the repository for S-DCNet, presented in our paper in the ICCV 2019:

From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting

Haipeng Xiong1, Hao Lu2, Chengxin Liu1, Liang Liu1, Zhiguo Cao1, Chunhua Shen2

1Huazhong University of Science and Technology, China

2The University of Adelaide, Australia

News !!!

An extended version of S-DCNet, i.e., SS-DCNet is now available !!!

Contributions

  • Reformulating the counting problem: We propose S-DCNet, which transforms open-set counting into a closed-set problem via Spatial Divide-and-Conquer;
  • Simple and effective: S-DCNet achieves the state-of-the-art performance on three crowd counting datasets (ShanghaiTech, UCF_CC_50 and UCF-QNRF), a vehicle counting dataset (TRANCOS) and a plant counting dataset (MTC). Compared to the previous best methods, S-DCNet brings a 20.2% relative improvement on the ShanghaiTech Part_B, 20.9% on the UCF-QNRF, 22.5% on the TRANCOS and 15.1% on the MTC.

Environment

Please install required packages according to requirements.txt.

Data

Testing data for ShanghaiTech dataset have been preprocessed. You can download the processed dataset from:

Baidu Yun (314M) with code: ou3b

Google Drive (314M)

Model

Pretrained weights can be downloaded from:

Baidu Yun (210MB) with code: 1tcb

Google Drive (210MB)

A Quick Demo

  1. Download the code, data and model.

  2. Organize them into one folder. The final path structure looks like this:

-->The whole project
    -->Test_Data
        -->SH_partA_Density_map
        -->SH_partB_Density_map
    -->model
        -->SHA
        -->SHB
    -->Network
        -->class_func.py
        -->merge_func.py
        -->SDCNet.py
    -->SHAB_main.py
    -->main_process.py
    -->Val.py
    -->load_data_V2.py
    -->IOtools.py
  1. Run the following code to reproduce our results. The MAE will be SHA: 57.575, SHB: 6.633. Have fun:)

    python SHAB_main.py
    

References

If you find this work or code useful for your research, please cite:

@inproceedings{xhp2019SDCNet,
  title={From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer},
  author={Xiong, Haipeng and Lu, Hao and Liu, Chengxin and Liang, Liu and Cao, Zhiguo and Shen, Chunhua},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2019},
  pages = {8362-8371}
}

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Implementaion of S-DCNet (ICCV 2019)

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