Model Card for rebotnix/rb_crowhuman
Person and Head Detection – Trained by KINEVA, Built by REBOTNIX, Germany Current State: in production and re-training.
rb_crowhuman is a specialized object detection model trained on the CrowdHuman dataset to detect persons and heads in densely crowded scenes. Designed for reliable detection in challenging conditions with heavy occlusion, overlapping bodies, and varying crowd densities, this model is well suited for crowd analysis, people counting, and safety monitoring in public spaces.
Developed and maintained by REBOTNIX, Germany, https://rebotnix.com
About KINEVA
KINEVA is an automated training platform based on the MCP Agent system. It regularly delivers new visual computing models, all developed entirely from scratch. This approach enables the creation of customized models tailored to specific client requirements, which can be retrained and re-released as needed. The platform is particularly suited for applications that demand flexibility, adaptability, and technological precision—such as industrial image processing, smart city analytics, or automated object detection.
KINEVA is continuously evolving to meet the growing demands in the fields of artificial intelligence and machine vision. https://rebotnix.com/en/kineva
Example Predictions
| Input Image | Detection Result |
|---|---|
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Model Details
- Architecture: KINEVA SILVER (custom training head with optimized anchor boxes)
- Task: Person and Head Detection (2 classes: person, head)
- Trained on: CrowdHuman dataset
- Format: PyTorch
.pth+ ONNX and TRT export available on request - Training Framework: PyTorch + KINEVA + custom augmentation
We're happy to license or provide access to all intermediate weights for research or further development purposes. Please feel free to reach out.
Dataset
The model was trained on the CrowdHuman dataset, featuring:
- Over 15,000 images with highly crowded scenes
- Annotations for full body (person) and head bounding boxes
- Heavy occlusion and overlapping individuals
- Diverse indoor and outdoor environments
More on CrowdHuman: https://www.crowdhuman.org
Intended Use
| Intended Use | Not Intended Use |
|---|---|
| Person and head detection in crowded scenes | Surveillance without human review |
| Crowd density estimation and people counting | Military / lethal applications |
| Public safety and occupancy monitoring | Real-time safety-critical decisions without human oversight |
| Retail analytics and footfall analysis | Individual identification or tracking |
Limitations
- May yield false positives in scenes with mannequins or posters depicting people
- Not fine-tuned for thermal or night vision imagery
- Heavy occlusion in extremely dense crowds may reduce detection accuracy
- Performance may degrade on low-resolution or distant subjects
Usage Example
from kineva import KINEVA
#initialize model
model = KINEVA(model="models/kineva_crowdhuman.pth")
#run inference on image
final_boxes, final_scores, final_labels = model.detect("example_crowdhuman1.jpg", threshold=0.35)
#draw detection
model.draw(final_boxes, final_scores, final_labels, output_path="./output_1.jpg")
Contact
For commercial use or re-training this model support, or dataset access, contact:
REBOTNIX
Email: communicate@rebotnix.com
Website: https://rebotnix.com
License
This model is released under CC-BY-NC-SA unless otherwise noted. For commercial licensing, please reach out to the contact email.