F2DNet is a Pedestron based repository which implements a novel, two-staged detector i.e. Fast Focal Detection Network for pedestrian detection.
Pedestron is a MMdetection based repository, that focuses on the advancement of research on pedestrian detection. We provide a list of detectors, both general purpose and pedestrian specific to train and test. Moreover, we provide pre-trained models and benchmarking of several detectors on different pedestrian detection datasets. Additionally, we provide processed annotations and scripts to process the annotation of different pedestrian detection benchmarks. If you use Pedestron, please cite us (see at the end) and other respective sources.
- 🧨 We have realeased PedesFormer - Transformer Based Pedestrian Detection repo (particularly Swin Transformer) along with pre-trained models. Stay tune for the updates. 🧨
- Caltech and EuroCity Persons. Pre-Trained model available.
Please refer to base repository for step-by-step installation.
In addition to configuration for different detectors provided in base repository we provide configuration for F2DNet.
Please refer to base repository for dataset preparation.
| 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 |
| Dataset | Config | Model | ↓Reasonable | ↓Small | ↓Heavy |
|---|---|---|---|---|---|
| CityPersons | cascade_hrnet | Cascade Mask R-CNN | 7.5 | 8.0 | 28.0 |
| CityPersons | ecp_cp | F2DNet | 7.8 | 9.4 | 26.2 |
| Caltech Pedestrian | cascade_hrnet | Cascade Mask R-CNN | 1.7 | 25.7 | |
| Caltech Pedestrian | ecp_cp_caltech | F2DNet | 1.7 | 2.1 | 20.4 |
@Article{khan:2022,
author = {Khan, Abdul Hannan AND Munir, Mohsin AND Elst, Ludger van AND Dengel, Andreas},
title = {F2DNet: Fast Focal Detection Network for Pedestrian Detection},
version = {1},
date = {2022-03-04},
eprinttype = {arxiv},
eprintclass = {cs.CV, I.2.10; I.4.8; I.5.4},
eprint = {http://arxiv.org/abs/2203.02331v1},
url = {http://arxiv.org/abs/2203.02331v1}
}
@article{hasan2022pedestrian,
title={Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond},
author={Hasan, Irtiza and Liao, Shengcai and Li, Jinpeng and Akram, Saad Ullah and Shao, Ling},
journal={arXiv preprint arXiv:2201.03176},
year={2022}
}