https://github.com/behdadsdp/f2dnet

[F2DNet] Fast Focal Detection Network for Pedestrian Detection

https://github.com/behdadsdp/f2dnet

Repository

[F2DNet] Fast Focal Detection Network for Pedestrian Detection

https://github.com/BehdadSDP/F2DNet/blob/master/

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/f2dnet-fast-focal-detection-network-for/pedestrian-detection-on-caltech)](https://paperswithcode.com/sota/pedestrian-detection-on-caltech?p=f2dnet-fast-focal-detection-network-for)

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/f2dnet-fast-focal-detection-network-for/pedestrian-detection-on-citypersons)](https://paperswithcode.com/sota/pedestrian-detection-on-citypersons?p=f2dnet-fast-focal-detection-network-for)

# F2DNet



F2DNet is a [Pedestron](https://github.com/hasanirtiza/Pedestron) based repository which implements a novel, two-staged detector i.e. Fast Focal Detection Network for pedestrian detection.

 

### Installation
Please refer to [base repository](https://github.com/hasanirtiza/Pedestron) for step-by-step installation. 

### List of detectors

In addition to configuration for different detectors provided in [base repository](https://github.com/hasanirtiza/Pedestron) we provide configuration for F2DNet.


### Following datasets are currently supported 
* [Caltech](http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/)
* [CityPersons](https://github.com/cvgroup-njust/CityPersons)
* [EuroCity Persons](https://eurocity-dataset.tudelft.nl/)

### Datasets Preparation
Please refer to base repository for dataset preparation.


# Benchmarking 
### Benchmarking of F2DNet on pedestrian detection datasets
| Dataset            | ↓Reasonable |  ↓Small   |  ↓Heavy   | 
|--------------------|:----------:|:--------:|:--------:|
| CityPersons        |  **8.7**   | **11.3** | **32.6** | 
| EuroCityPersons    |    6.1     |   10.7   |   28.2   | 
| Caltech Pedestrian |  **2.2**   | **2.5**  | **38.7** |

### Benchmarking of F2DNet when trained using extra data on pedestrian detection datasets
| Dataset            | Config                                                                                                       | Model                                                                                        | ↓Reasonable | ↓Small |  ↓Heavy   | 
|--------------------|--------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------|:----------:|:------------:|:--------:|
| CityPersons        | cascade_hrnet | Cascade Mask R-CNN                                                                           |  **7.5**   |   **8.0**    |   28.0   |
| CityPersons        | [ecp_cp](https://github.com/AbdulHannanKhan/F2DNet/blob/master/configs/f2dnet/cp/ecp_sup.py)                 | [F2DNet](https://drive.google.com/file/d/1IrwvdLtpOjUpmz2_IXWENbVNAQtEZKn-/view?usp=sharing) |    7.8     |     9.4      | **26.2** |
| Caltech Pedestrian | cascade_hrnet | Cascade Mask R-CNN                                                                           |  **1.7**   |              |   25.7   |
| Caltech Pedestrian | [ecp_cp_caltech](https://github.com/AbdulHannanKhan/F2DNet/blob/master/configs/f2dnet/caltech/ecp_cp_sup.py) | [F2DNet](https://drive.google.com/file/d/1DzcKR-tKy-Oa6uVoiYUt_q_7h5iwwCeh/view?usp=sharing)                                                                                   |  **1.7**   |   **2.1**    | **20.4** |


# References

* [Pedestron](https://openaccess.thecvf.com/content/CVPR2021/papers/Hasan_Generalizable_Pedestrian_Detection_The_Elephant_in_the_Room_CVPR_2021_paper.pdf)

### Please cite the following work
[AxXiv2022](https://arxiv.org/pdf/2203.02331.pdf)
```
@inproceedings{khan2022f2dnet,
  title={F2DNet: fast focal detection network for pedestrian detection},
  author={Khan, Abdul Hannan and Munir, Mohsin and van Elst, Ludger and Dengel, Andreas},
  booktitle={2022 26th International Conference on Pattern Recognition (ICPR)},
  pages={4658--4664},
  year={2022},
  organization={IEEE}
}
```

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