philipho/irail-crowd-counting-yolov8n

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=0.25
• —iou=0.75
• —max_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_heads

Baseline 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