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.

Performance is essential for video analytic applications since algorithms are usually computationally heavy and such systems are supposed to work almost in real time. From one side it can be increased by improving & changing algorithms. This is a major way since it allows to increase performance dramatically. From another side performance can be increased little bit more by relatively simple way – using of good compiler and by tuning of compile options. Let see how it can be done in real programs.

A modern video analytic system depending on business/customer requirements should work in different situations/conditions. Complex, noisy background with many different objects/textures, changing lighting conditions, shadows, lack of light, weather conditions (for outdoor system installations) like rain, snow, fog and others, motion blur, camera movements, cameral sensor quality, camera resolution, camera focus issues, camera internal optimizations, color temperature, end many other factors make development of the good object recognition software a challenging, almost impossible task. In addition, the usual requirement is that the system should work in real time, which makes this task even more difficult.

2D face recognition is an extensively studied, but still evolving subject of research. Various strategies including statistical approaches, hidden Markov models, neural networks, template based and feature based matching have been proposed. Here we briefly present our implementation which is based on past research and achieves state-of-the-art recognition performance on considerably low resolution input facial images.
Our approach can be divided into three independent phases: Facial landmarks library construction (offline), Building of facial descriptor (once per novel image) and Facial descriptors matching.

Currency recognition seems to be one of the popular topic in “applied” computer vision. There are a lot of articles, blog entries describing different approaches to currency recognition. In this post I’ll share my experience of using so-called HMAX model.

Build platform: Ubuntu 9.10
Target platform: Android

Download and prepare OpenCV library source code.

1. Download the latest version of OpenCV (http://sourceforge.net/project/showfiles.php?group_id=22870).

2. As build platform is Linux, select linux version (for example OpenCV2.1.0.tat.bz).

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.

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.

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