Open-Source Reference Implementations
Deploy your own IoT solutions by using these prebuilt open-source projects.
Deploy sensor fusion technology for an automated checkout that enables real-time insight about the products consumers are buying using the EdgeX Foundry* extensible framework.
- Authenticate and authorize different users.
- Develop add-on services and sensors.
- Recognize items, detect discrepancies, and record real-time data.
Apply the pretrained models in the Intel® Distribution of OpenVINO™ toolkit to detect and count the number of people waiting in lines in real time.
- Infer crowd information based on line size and historic data.
- Generate insight and provide recommendations based on historic line size information.
Detect shoppers and determine their walking direction. Get alerts for shoppers walking opposite to the predefined direction.
- Analyze walking patterns and provide recommendations.
- Implement social distancing guidelines in retail markets.
Detect loss at self-checkout by seamlessly connecting different sensor devices, including weight scale sensors, cameras, and RFIDs.
- Recognize products entering and exiting retail checkout areas.
- Develop add-on services and sensors.
- Use multiple edge sensors to accurately recognize items, detect discrepancies, and record a real-time transaction log (RTTL).
Build a solution to analyze customer expressions and reactions to product advertising collateral that is positioned on retail shelves.
- Measure active versus inactive user product engagement.
- Capture analytics on shopper reactions to visual ads.
Detect the mood of shoppers as they look at a retail or kiosk display.
- Mall shoppers using interactive or map kiosk
- Grocery store shoppers viewing digital signage ads
- Hospitals using a kiosk to assist patients or visitors
Use computer vision inference in the Intel® Distribution of OpenVINO™ toolkit to provide analytics on customer engagement, store traffic, and shelf inventory.
- Detect people within a designated area by displaying a green bounding box over them, count the total number of people, and the time they are in the frame.
- Use an inferencing pipeline to detect faces, emotions, and head poses.
Build a solution that recognizes people within a specific area and measures the distance between them. Get an alert if the distance is less than a specified amount.
- Measure the distance between shoppers in malls and retail stores.
- Generate insight for contact tracing based on social distancing data.
- Understand retail store capacity to maintain safe social distancing.
Find people who cross a virtual line and determine whether they are entering or exiting a store. Count the unique shoppers and then display the current store occupancy.
- Review people entering and exiting retail checkout areas.
- Ensure the store occupancy does not exceed a predefined limit.
- Infer crowd information based on history of store capacity.
Monitor three different streams of video that count people inside and outside of a facility. This application also counts product inventory.
- Movement of people
- Foot activity in retail or warehouse spaces
- Inventory availability of products on shelves
Segment brain tumors in raw MRI images by applying the U-Net architecture.
- Detect brain tumors in MRI images.
- Plot predictions from segmented brain tumors.
- Predict results using a pretrained model and the Sørensen–Dice coefficient.
Detect pneumonia in X-rays using computer vision inferencing and a pretrained model.
- Predict the probability of infection caused by pneumonia.
- Identify anomalies and predict results with medical imaging.
- Train models for classification using labeled X-rays from open-source datasets.
Predict performance issues with manufacturing equipment motors. Perform local or cloud analytics of the issues found, and then display the data on a user interface to determine when failures might arise.
- Air conditioning units
Create a concurrent video analysis pipeline featuring multistream face and human pose detection, vehicle attribute detection, and the ability to encode multiple videos to local storage in a single stream.
- Retail digital surveillance such as network video recorders
- Video matrix commercial multimedia applications
- Video conference multipoint control units (MCU) and terminals
Build an application that alerts you when someone enters a restricted area. Learn how to use models for multiclass object detection.
- Record and send alerts on activity in controlled spaces.
- Track parking lots, entrances, and property.
Implement and use Intel® hardware platforms for video decoding, encoding, and optimization using various media stacks.
- Transmit video into a computer vision application for people detection.
- Use GStreamer* and the Intel® Media SDK to capture video streams and encode them into a format that can be stored on a server.
Receive or post information on available parking spaces by tracking how many vehicles enter and exit a parking lot.
- Track and analyze vehicle activity.
- Report on parking space availability.
Create a smart video application using the Intel Distribution of OpenVINO toolkit. The toolkit uses models and inference to run single-class object detection.
- Track activity in retail.
- Observe factory work spaces and building entrances for activity.
- Capture and record information on the number of people.
Secure work areas and send alerts if someone enters the restricted space.
- Track worker activity in proximity to heavy machinery.
- Develop safety solutions using computer vision technologies.
Observe workers as they pass in front of a camera, identify them using facial recognition, and determine if they have adequate safety protection.
- Ensure safety in the industrial workplace.
- Detect the presence of required safety equipment.
- Monitor factories, warehouses, etc.