iRail Crowd Counting (YOLOv8n) — Head Detection Baseline
This repository contains a YOLOv8n (nano) model fine-tuned for pedestrian head detection in crowded environments (railway platforms + event entrances).
The main objective is crowd counting per frame by detecting heads and counting bounding boxes.
This model is designed as an educational baseline for an iRail/Azure project extension where the iRail API does not provide occupancy, so we estimate a proxy crowd level from images/frames.
Model
- Architecture: YOLOv8n (Ultralytics)
- Task: Object detection
- Class(es):
head (single class)
- Training input size: imgsz=832
- Baseline inference settings used for counting eval:
conf=0.25iou=0.75max_det=300
Dataset
Trained on RPEE-Heads (Railway Platforms and Event Entrances-Heads) with head bounding boxes.
- 1,886 images
- 109,913 head annotations
- Split:
- Train: 1,346 images
- Val: 246 images
- Test: 294 images
- License: CC BY-SA 4.0 (dataset + derived model share-alike requirements apply)
Paper reference:
RPEE-Heads Benchmark: A Dataset and Empirical Comparison of Deep Learning Algorithms for Pedestrian Head Detection in Crowds
Mohamad Abubaker, Zubaida AlSadder, Hamed Abdelhaq, Maik Boltes, Ahmed Alia
DOI: 10.34735/ped.2024.2
Dataset URL:
http://ped.fz-juelich.de/da/2024rpee_headsBaseline Evaluation (Detection)
Ultralytics validation metrics on Val/Test (single class: head):
Validation (246 images / 16,022 instances)
- Precision: 0.910
- Recall: 0.805
- mAP@0.50: 0.881
- mAP@0.50:0.95: 0.522
Test (294 images / 15,285 instances)
- Precision: 0.908
- Recall: 0.803
- mAP@0.50: 0.878
- mAP@0.50:0.95: 0.515
Crowd Counting Evaluation (Counting boxes per image)
Counting is computed as:
predicted_count = number of detected boxes (after NMS at chosen conf and iou).
Baseline counting settings:
imgsz=832, conf=0.25, iou=0.75
Metrics
- MAE: 4.67
- RMSE: 8
- Bias (pred - gt): 0.097 (slight undercount)
Intended Use
- Educational demo of:
- fine-tuning YOLOv8 for head detection
- evaluating detection metrics
- converting detections to a crowd count proxy
- A building block for a larger iRail/Azure pipeline (occupancy proxy)
Limitations
- Not trained on Belgian station camera viewpoints specifically.
- Counting via “number of boxes” can undercount in very dense crowds.
- Domain shift (camera height, lens distortion, resolution, lighting) may reduce performance.
Author
Amine Samoudi - GitHub:
@AmineSam
