S-DCNet

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

From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer

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

¹Huazhong University of Science and Technology, China

²The University of Adelaide, Australia

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

Download the code, data and model.
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

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}
}

xwjBupt/S-DCNet