OpenNCC compatible with OpenVINO utilizes edge-ai and IR LED technology to protect face privacy, keep 24 hours working but avoid producing light pollution.

San Jose, CA -- (ReleaseWire) -- 10/12/2021 --By 2025, it is expected that there will be 1 billion cameras in the public and private fields. The edge-ai cameras with deep learning and visual analysis will also be developed rapidly with the maturity of artificial intelligence technology and the improvement of computing power. At the same time, there will be a large number of application scenarios requiring privacy protection:

- Privacy and identity protection in the public/private sphere
- Online protection of children (i.e., Blur the faces of minors when uploading photos)
- Hospital bed care for medical health
- Online fitness training guide
- Elderly care monitoring, fall detection alarm

Though the demand is increasing, data security is being paid more and more attention to by countries all over the world. More than 100 countries are actively dealing with data security issues and legislating for protection:

- GDPR(Europe)
- THE PRIVACY ACT OF 1974 (US)
- Privacy Act 1988 (Australia)
- Personal Information Protection Act (Japan)
- Personal Data Protection Act (Singapore)
- The Cybersecurity Law of the People's Republic of China (China)

Vision is a kind of natural language. The face is the most personal feature of privacy. Protecting people's face information makes us more at ease and makes artificial intelligence better serve everyone in the field of vision.

How to implement a vision system with face privacy protection? OpenNCC Nighthawk is born right for this.

Generally speaking, a complete edge-ai camera is composed of a lens, photoelectric sensor, image processing chip, VPU/NPU processing unit, and some external interfaces. In daily applications, the camera will display photos and videos on the terminal computer, or transmit them to the cloud through the internet.

OpenNCC(Open Neural Compute Camera) is an edge AI camera based on Intel Movidius VPU and compatible with the Intel distribution of Intel OpenVINO toolkit. It is open and there is a lot of source code for reference on GitHub so that developers can deploy different deep learning models to achieve differentiated functions and performance. Developers only need to care about the implementation and application of the algorithm but don't need to spend a lot of time on the optical and hardware design of the camera and the basic imaging of the camera.

It takes only three steps to deploy an edge-ai face blurring night vision camera with OpenNCC.

Step 1: Deploy Face Detection Model on OpenNCC

OpenNCC camera supports OpenVINO. For reference, select an object detection model face-detection-retail-0004 from the pre-trained open model zoo of Intel OpenVINO Toolkit to implement face detection.

After deployment, this model can be used to realize real-time face detection of 30 FPS in the case of HD 1080 P. For specific deployment steps, refer to the OpenNCC blog article "How to deploy an intel open zoo model".

Step 2: Face blurring on camera

The pre-trained model face-detection-retail-0004 also gives coordinates of every face area, which consists of the start/end X, and start/end Y of the face bounding box. Use this information to extract the facial ROI, and then use an efficient and simple pixel substitution algorithm to realize face pixel blurring on OpenNCC.

Step 3: Integrate IR LED kit

In the field of medical treatment and nursing, cameras are often required to have good night vision ability so that they can adapt to 24-hour coverage. Therefore, providing an edge-AI face blurring night open camera solution that can meet privacy requirements can better support developers of these scenes to integrate their own applications.

The CMOS sensor of the Edge-AI camera is like human eyes, which converts light into digital images. The frequency of the electromagnetic waves that can be perceived by our eyes is 380 ~ 750 THz and the wavelength is between 400 ~ 780 nm. The sensing curve of a sensor used by OpenNCC has a good response rate in the band perceived by human eyes. However, at night, the external light is much weaker. For a camera with a fixed aperture, it can't get as much light as the human eyes. In this case, external lighting is needed to help improve the lighting conditions and ensure the imaging quality.

One way is to use the visible light within the band of 400-780 nm to supplement the light, just like our home lighting. However, in some special application scenarios, strong supplementary light at night will form light pollution and affect everyone's rest. In this case, using infrared light to fill the light is a better choice. By using the 850 nm infrared light to fill the light for the edge AI camera, the band exceeds the most sensitive sensing range of the human eyes and can well balance the problem of light filling and environmental light pollution.

OpenNCC camera provides such IR LED PCBA, which is easy to integrate into standard OpennNCC components, supports automatic detection of external light, supports automatic switching between day and night, and ISP optimization ready for day and night respectively.

Face blurring algorithm with IR LED night vision SKU: OpenNCC Nighthawk

About EyeCloud.AI
EyeCloud.AI is a leading supplier of open AI vision production solutions. We help vision developers overcome the integration and production challenges of delivering edge-AI vision products through our expertise in custom hardware production, embedded software, IoT management, and cloud services.