myAudience promo video

Rhonda Software will participate in Customer Engagement Technology World (CET World) in San Francisco, April 27-28, 2011. We are pleased to invite you to visit our booth #235.

We’ll be glad to have this chance to introduce you our innovative system myAudience – tool for automated audience measurement for digital signage, kiosks, showcases and many others. You can click this special link to register.

Your special PRIORITY CODE will automatically appear with your registration, giving you a FREE exhibits only pass. For up-to-date information about Customer Engagement Technololgy World, please visit www.CETworld.com.

people_counter.avi

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.

 The fact is that most of organization have own IP or CCTV camera based security infrastructure. And also often there is a camera which is already top-mounted. Thus top-mounted camera counting approach is looked very perspective from reusing infrastructure point of view.

We researched a lot of approaches. There are a lot of ready methods for people counting with top-mounted camera. But such methods either are patented or don’t meet our expectation in quality or speed. Thus we developed own method (see demo video).

Method counts human each time they cross a predefined counting line (that is why it is often called as “line-crossing”). The assumption is that the line should be selected orthogonally to the main people flow.

Now we skip implementation details of the method. If shortly, our method is a real time 15 fps+, we tested it usual USB or IP cameras. We tested our method in indoor and outdoor use-case and the quality is 80-90% (dependent on environment condition).

Our method can be used in other applications, e.g. vehicle counting, you could look at the video how it works. Have a nice watching…

tools.avi

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. Theoretically, the algorithm works with any “3D” objects which have good projection on 2D coordinates. However, natural 3D objects are covered by few templates for set of 2D projections. For limited number of object templates, the algorithm works in real-time on PC (Intel P4 3.0 GHz) .

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

nike.avi

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

The algorithm is a cross-platform solution.

Performance:

• on ARM11 530MHz, the algorithm gives 1 fps for 640×480 frame;
• on Intel P4 3Hz, the algorithm gives 12 fps and more for 640×480 frame.

Quality: 86%.

am-3.avi

This is a demo video of Rhonda Audience Measurement system (MyAudience product, www.MyAudience.com).

The system is able to recognize gender of a person. So, red rectangle is for a woman, dark blue rectangle is for a man. The quality of gender recognition algorithm is 90%.

The system works on Intel Atom: 10 fps and higher, and on Intel Pentium 4: 15 fps and higher. Besides, it is a cross-platform solution. It was tested on both Windows XP and Linux, and also it was tested on ARM Cortext-A8.

No accelerations (CUDA, fixed point, etc) are used! So, the solution has great potential for improvements.

barcodes-2.avi

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.

Description

In this post we present a video demo of the gender classifier. This classifier is adapted for frontal- and near to frontal-oriented faces. It is capable to provide the real-time gender recognition with the invariance to complicated lighting conditions. The foundation of the implemented method is an AdaBoost powered extraction of the gender-descriptive features along with the further separation of male / female subsets for learning of the decision-making routine.
The classifier works in the conjunction with face-detector and tries to classify all found faces on the each frame. The achieved accuracy of correct classification is 90-92%, though on small faces (less then 32×32 pixels) returned by face-detector the accuracy of gender recognition could reduce to 80-88%.

PTZ-3_sonicfire.avi

Description

On this demo CV system tracks moving people using single PTZ (pan/tilt/zoom) camera (AXIS 214) and tries to positioning it to always keep first entered person in the camera sight. When PTZ stops in new position, the system filters out still objects from those that actually moving, assigns unique IDs (and color frames) to them and measures proximity of these objects to the original one using color-histogram-based algorithm. The object with highest proximity will be treated as a target. System will turn camera in the direction where targeting object moves, when it is approach to the border of camera sight (the red rectangle on the border indicates direction of next movement of PTZ cam).