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PWC

PWC

Pedestron

Pedestron is a MMetection 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 consider citing respective sources.

🔥 Updates 🔥

  • [NEW] Ported testing of Mask-Guided Attention Network for Occluded Pedestrian Detection (MGAN). (ICCV'19), into Pedestron along with pretrained model
  • [NEW] Added a pre-trained model for generic person detection
  • [NEW] Pedestron now supports general person and pedestrian detection in crowded scenarios. Pre-trained model along with evaluation script on CrowdHuman benchmark is added
  • [NEW] Pre-trained model for WIDER pedestrian datasets has been added to the Pedestron, for general person detection
  • 🔥 Configuration along with a pre-trained model for RetinaNet with Gudied Anchoring added. Its fast and accurate
  • 🔥 Configuration along with a pre-trained model for Faster R-CNN with HRNet for EuroCity Persons has been adeed

YouTube demo

Installation

We refer to the installation and list of dependencies to installation file. Clone this repo and follow installation.

List of detectors

Currently we provide configurations for with different backbones

  • Cascade Mask-R-CNN
  • Faster R-CNN
  • RetinaNet
  • RetinaNet with Guided Anchoring
  • Hybrid Task Cascade (HTC)
  • MGAN

Following datasets are currently supported

Datasets Preparation

Benchmarking

Benchmarking of pre-trained models on pedestrian detection datasets (autonomous driving)

Detector Dataset Backbone Reasonable Heavy
Cascade Mask R-CNN CityPersons HRNet 7.5 28.0
Cascade Mask R-CNN CityPersons MobileNet 10.2 37.3
Faster R-CNN CityPersons HRNet 10.2 36.2
RetinaNet CityPersons ResNeXt 14.6 39.5
RetinaNet with Guided Anchoring CityPersons ResNeXt 11.7 41.5
Hybrid Task Cascade (HTC) CityPersons ResNeXt 9.5 35.8
MGAN CityPersons VGG 11.2 52.5
Cascade Mask R-CNN Caltech HRNet 1.7 25.7
Cascade Mask R-CNN EuroCity Persons HRNet 4.4 21.3
Faster R-CNN EuroCity Persons HRNet 6.1 27.0

Benchmarking of pre-trained models on general human/person detection datasets

Detector Dataset Backbone AP
Cascade Mask R-CNN CrowdHuman HRNet 84.1

Pre-Trained models

Cascade Mask R-CNN

  1. CityPersons
  2. Caltech
  3. EuroCity Persons
  4. CrowdHuman 1
  5. CrowdHuman 2 (higher AP)
  6. WIDER Pedestrian

Faster R-CNN

  1. CityPersons
  2. EuroCity Persons

RetinaNet

  1. CityPersons

RetinaNet with Guided Anchoring

  1. CityPerson

Hybrid Task Cascade (HTC)

  1. CityPersons

MGAN

  1. CityPersons

Getting Started

Running a demo using pre-trained model on few images

  1. Pre-trained model can be evaluated on sample images in the following way
python tools/demo.py config checkpoint input_dir output_dir

Download one of our provided pre-trained model and place it in models_pretrained folder. Demo can be run using the following command

python tools/demo.py configs/elephant/cityperson/cascade_hrnet.py ./models_pretrained/epoch_5.pth.stu demo/ result_demo/

Training

  • single GPU training
  • multiple GPU training

Train with single GPU

python tools/train.py ${CONFIG_FILE}

Train with multiple GPUs

./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]

For instance training on CityPersons using single GPU

python tools/train.py configs/elephant/cityperson/cascade_hrnet.py

Training on CityPersons using multiple(7 in this case) GPUs

./tools/dist_train.sh configs/elephant/cityperson/cascade_hrnet.py 7

Testing

  • single GPU testing
  • multiple GPU testing

Test can be run using the following command.

python ./tools/TEST_SCRIPT_TO_RUN.py PATH_TO_CONFIG_FILE ./models_pretrained/epoch_ start end\
 --out Output_filename --mean_teacher

For example for CityPersons inference can be done the following way

  1. Download the pretrained CityPersons model and place it in the folder "models_pretrained/".
  2. Run the following command:
python ./tools/test_city_person.py configs/elephant/cityperson/cascade_hrnet.py ./models_pretrained/epoch_ 5 6\
 --out result_citypersons.json --mean_teacher

Alternatively, for EuroCity Persons

python ./tools/test_euroCity.py configs/elephant/eurocity/cascade_hrnet.py ./models_pretrained/epoch_ 147 148 --mean_teacher

or without mean_teacher flag for MGAN

python ./tools/test_city_person.py configs/elephant/cityperson/mgan_vgg.py ./models_pretrained/epoch_ 1 2\
 --out result_citypersons.json

Testing with multiple GPUs on CrowdHuman

./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}]
./tools/dist_test.sh configs/elephant/crowdhuman/cascade_hrnet.py ./models_pretrained/epoch_19.pth.stu 8 --out CrowdHuman12.pkl --eval bbox

Please cite the following work

ArXiv version

@article{hasan2020pedestrian,
  title={Pedestrian Detection: The Elephant In The Room},
  author={Hasan, Irtiza and Liao, Shengcai and Li, Jinpeng and Akram, Saad Ullah and Shao, Ling},
  journal={arXiv preprint arXiv:2003.08799},
  year={2020}
}