Important Notice
This model is provided for internal evaluation and development purposes only. It is not validated for and must not be used in clinical, diagnostic, or production settings. See #use-and-limitations and #License.
Model Information
Description
people-present is a single-stage object detection model that detects persons present in a frame. It uses the same MobileNetV2 + FPN + anchor-free head architecture as the companion patient-present model, retrained on a person-detection objective. In the a NICU Warmer reference pipeline, its bounding boxes are cross-referenced with patient-present detections via IoU overlap to classify each detected person as either "caretaker" or "patient". This model is an internal development and evaluation tool produced as part of Intel's NICU Warmer reference design and has not undergone clinical validation. In this reference application, the detection of another person in the boxed space in the video frame (other than the “patient”) is designed to mimic the workload of an AI model operating in a real neonatal scenario where it would be able to detect a caretaker or other hospital worker near the NICU Warmer.
Intended Use
This model is intended for use by software developers and researchers evaluating AI-assisted monitoring workflows on Intel hardware. It demonstrates person detection in warmer video as a building block for caretaker-presence sensing. people-present itself does not provide any medical functionality, nor is it intended to process or interpret medical data for a medical purpose. Developers are responsible for independently validating and adapting people-present for their specific use case. It must not be used in live clinical environments or for any diagnostic or patient-safety purpose.
Technical Specifications
| Attribute | Detail |
|---|---|
| Architecture | MobileNetV2 backbone + FPN neck + anchor-free detection head |
| Parameters | ~5–7M (FP32) |
| Input | 992×800 RGB image (W×H), float32, normalized [0,1], NCHW |
| Output | [N, 5] — bounding boxes (x1, y1, x2, y2, confidence), post-NMS, pixel coords at input resolution |
| Training hardware | Intel Ultra Core |
| Framework | PyTorch (mmdetection) → OpenVINO IR FP32 |
Training Data
The model was trained using images of a box-like compartment taken from above. These images were created by the development team for this purpose. Within the box compartment (representing the NICU Warmer), numerous images show different configurations of scenarios i.e. the presence or absence of a plastic figure (representing the “patient”), the presence or absence of a hand (representing the “caretaker”) and the presence or absence of latch clips (representing the NICU Warmer hood latch). The model is trained to recognise the presence a hand within this environment. Once trained, you can test the performance of the AI model and by extension the hardware in real-time to mimic production use. Whilst the training process allows for simulation of production workloads, the fact that the training data has no clinical nexus demonstrates that the NICU Warmer reference application was not intended for use within a production, clinical environment.
Evaluation
Evaluation was limited to confirmation that the model can recognise the presence of a hand in the environment described above. No formal evaluation of performance was undertaken. It is recognised that reference AI workloads may not replicate real, production workloads
Use and Limitations
Permitted Uses
- Evaluation and benchmarking of Healthcare and Life Sciences AI workflows
- Research and development
- Academic study
Prohibited Uses
- Clinical or diagnostic use
- Production deployment
- Use with live patients or real patient data
Known Limitations
- No evaluation of performance undertaken
- No evaluation of whether reference AI workloads will replicate real, production workloads
License
The use of people-present is governed by the Intel Limited Internal Research & Development Use License Agreement. By accessing this model, you agree to the license terms.