Pedestrian Detection

PennFudanPed_train.json: Contains COCO annotations for a randomly generated train split of the PennFudan dataset.

PennFudanPed_val.json: Contains COCO annotations for the corresponding validation split of the PennFudan dataset.

The below scripts should be run for detections obtained using all the three methods mentioned below:

1. Pretrained HoG
2. Custom HoG trained using SVM on HoG features
3. Pretrained Faster RCNN

Installation

git clone https://github.com/sm354/Pedestrian-Detection.git
cd Pedestrian-Detection
pip install -r requirements.txt

Download Penn-Fudan Dataset

wget https://www.cis.upenn.edu/~jshi/ped_html/PennFudanPed.zip
unzip PennFudanPed.zip

Download SVM weights for Custom HoG Detector

gdown <link>
unzip svm.zip

Running Models

1. Pretrained HoG Detector

python eval_hog_pretrained.py --root <path to dataset root directory> --test <path to test json> --out <path to output json>

2. Custom HoG Detector

Training

python train_hog_custom.py --root <path to dataset root directory> --train <path to train json> --model <path to save trained SVM model>

Testing

python eval_hog_custom.py --root <path to dataset root directory> --test <path to test json> --out <path to output json> --model <path to trained SVM model>

3. Faster RCNN

python eval_faster_rcnn.py --root <path to dataset root directory> --test <path to test json> --out <path to output json>

Evaluation script

python eval_detections.py --gt <path to ground truth annotations json> --pred <path to detections json>

The script eval_detections.py takes in ground truth annotations and predicted detections for the evaluation dataset and computes the following metrics:

1. Average Precision, computed over 10 IOU thresholds in the range 0.5:0.05:0.95
2. Average Recall computed at 1 detection per image.
3. Average Recall comptued at 10 detections per image.

Authors

• Shubham Mittal
• Aditi Khandelwal