Multiview Detection with Shadow Transformer (and View-Coherent Data Augmentation) [arXiv] [paper]

@inproceedings{hou2021multiview,
  title={Multiview Detection with Shadow Transformer (and View-Coherent Data Augmentation)},
  author={Hou, Yunzhong and Zheng, Liang},
  booktitle={Proceedings of the 29th ACM International Conference on Multimedia (MM ’21)},
  year={2021}
}

Overview

We release the PyTorch code for MVDeTr, a state-of-the-art multiview pedestrian detector. Its superior performance should be credited to transformer architectures, updated loss terms, and view-coherent data augmentations. Moreover, MVDeTr is also very efficient and can be trained on a single RTX 2080TI. This repo also includes a simplified version of MVDet, which also runs on a single RTX 2080TI.

Content

• Dependencies
• Data Preparation
• Code Preparation
• Training
  • Architectures
  • Loss terms
  • Augmentations

MVDeTr Code

This repo is dedicated to the code for MVDeTr.

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Dependencies

This code uses the following libraries

• python
• pytorch & tochvision
• numpy
• matplotlib
• pillow
• opencv-python
• kornia

Data Preparation

By default, all datasets are in ~/Data/. We use MultiviewX and Wildtrack in this project.

Your ~/Data/ folder should look like this

Data
├── MultiviewX/
│   └── ...
└── Wildtrack/ 
    └── ...

Code Preparation

Before running the code, one should go to multiview_detector/models/ops and run bash mask.sh to build the deformable transformer (forked from Deformable DETR).

Training

In order to train classifiers, please run the following,

python main.py -d wildtrack
python main.py -d multiviewx

This should automatically return evaluation results similar to the reported 91.5% MODA on Wildtrack dataset and 93.7% MODA on MultiviewX dataset.

Architectures

This repo supports multiple architecture variants. For MVDeTr, please specify --world_feat deform_trans; for a similar fully convolutional architecture like MVDet, please specify --world_feat conv.

Loss terms

This repo supports multiple loss terms. For the focal loss variant as in MVDeTr, please specify --use_mse 0; for the MSE loss as in MVDet, please specify ----use_mse 1.

Augmentations

This repo includes support for view coherent data augmentation, which applies affine transformations onto the per-view inputs, and then invert the per-view feature maps to maintain multiview coherency.

Pre-trained models

You can download the checkpoints at this link.