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The face recognition demo shows person facial feature training via a single photo and subsequent face matching on the live video stream, using VisionLabs’ library integrated onto the H22 System on a Module (SoM).
This demo showcases real-time Human Pose Estimation, based on the Open Pose library, ported onto the camera platform, and designed by Rhonda’s Activity Recognition neural network for human behavior recognition. The two Deep Learning Neural Networks (DNN), along with the video pipeline, run on the Rhonda Software CV22 System on a Module (CV22 SoM).
Marketing researches are area where required to analyze a lot of data. E.g. we want to understand how many people are visiting a bank. In order to count this value, we need to count each man or woman which are entering to or exiting from the bank. For resolving this task there are a lot of approaches: e.g. use special gate with laser or mechanical counter. Though there are people counting tasks where such approaches cannot work or too unuseful. E.g. barrier cannot be used where people flow is very high, and laser counters have limitations as well.
Opposite the approaches above, we found papers where top-mounted camera is used for resolving the people counting task.
This object recognition algorithm is based on own pattern-matching algorithm. The algorithm is able to recognize pre-trained objects which are defined with special set of templates.
The currency recognition demo application works under Windows XP, Intel P4 3GHz. Quality of recognition: 85%. The solution is cross-platform. The application was tested on Linux, ARM11 and on Linux/Windows, Intel Atom.
Interesting details can be found at: http://www.computer-vision-software.com/blog/2009/10/barcode
This is a demo video of the invariant orientation and scale fast object detection algorithm. The algorithm is a robust in cases when the object is deformed a little 🙂
I would like to represent an executable demo which was described at http://www.computer-vision-software.com/blog/2009/07/barcode-recognition/.
This is demo application for Rhonda barcode recognition library. It’s cross-platform library written on C++ language. It was tested on ARM Cortex-A8, ARM11 and x86 platforms.
Average recognition time on 640×480 frames:
P4 – 3GHz: 25ms
This is a demo video of Rhonda Audience Measurement system (MyAudience product, www.MyAudience.com).
Description
Here you can find a demo of the barcode detection and recognition routine. The current version is set up to detect a barcode labels mostly oriented horizontally and vertically. The routine processes each frame of the video stream and scans it trying to detect a barcode starting position, relying on the appearance specific of the barcode labels. As long as a potential starting position detected the routine applies the set of the image filters to increase the readability of the scanned window. Then recognition algorithm tries both to read and validate the barcode label starting from the detected point. You may see for yourself that such combination of detection and recognition algorithms works pretty well.
This demo works with UPC-A and EAN-13 barcode types.