tcjcxy30/CrowdDet

Detection in Crowded Scenes: One Proposal, Multiple Predictions

This is the pytorch implementation of our paper "Detection in Crowded Scenes: One Proposal, Multiple Predictions", https://arxiv.org/abs/2003.09163, published in CVPR 2020.

Our method aiming at detecting highly-overlapped instances in crowded scenes.

The key of our approach is to let each proposal predict a set of instances that might be highly overlapped rather than a single one in previous proposal-based frameworks. With this scheme, the predictions of nearby proposals are expected to infer the same set of instances, rather than distinguishing individuals, which is much easy to be learned. Equipped with new techniques such as EMD Loss and Set NMS, our detector can effectively handle the difficulty of detecting highly overlapped objects.

The network structure and results are shown here:

Citation

If you use the code in your research, please cite:

@article{chu2020detection,
	title={Detection in Crowded Scenes: One Proposal, Multiple Predictions},
	author={Chu, Xuangeng and Zheng, Anlin and Zhang, Xiangyu and Sun, Jian},
	journal={arXiv preprint arXiv:2003.09163},
	year={2020}
}

Run

1. Requirements:

   • python3.6.8, pytorch 1.4.0, cuda10.0

2. CrowdHuman data:

   • CrowdHuman is a benchmark dataset to better evaluate detectors in crowd scenarios. The dataset can be downloaded from http://www.crowdhuman.org/. The path of the dataset is set in config.py.

3. Install some cuda tools:

   cd utils/det_tools_cuda
   python3 setup.py install
   

   • Please note that it must be compiled with cuda!

4. Steps to run:

   • Step1: training. More training and testing settings can be set in config.py.

   python3 train.py
   

   • Step2: testing. If you have multiple GPUs, you can use -d 0-1 to use more GPUs.

   python3 test.py -r 30
   

   • Step3: evaluating.

   python3 .evaluate/compute_APMR.py --detfile ./model/crowd_emd_simple/outputs/eval_dump/dump-30.json --target_key 'box'
   python3 .evaluate/compute_JI.py --detfile ./model/crowd_emd_simple/outputs/eval_dump/dump-30.json --target_key 'box'
   

Models

We use pre-trained model from Detectron2 Model Zoo: https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-50.pkl. (or R-50.pkl)

All models are based on ResNet-50 FPN.

	AP	MR	JI	Model
FPN Baseline	0.8713	0.4307	0.7929	fpn_baseline.pth
EMD Simple	0.9044	0.4251	0.8204	emd_simple.pth
EMD with RM	0.9063	0.4149	0.8245	emd_refine.pth

Contact

If you have any questions, please do not hesitate to contact Xuangeng Chu (xg_chu@pku.edu.cn).