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 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.
Posted on : 10-11-2009 | By : rhondasw | In : OpenCV
Hi All, before posting your question, please look at this FAQ carefully! Also you can read OpenCV haartraining article. If you are sure, there is no answer to your question, feel free to post comment. Also please, put comments about improvement of this post. This post will be updated, if needed.
Posted on : 09-11-2009 | By : rhondasw | In : OpenCV
Nowadays, different audience measurement systems become more and more popular. They are used in active advertising, for gathering statistics, etc. One of the key features of these smart systems is attention detection. For advertisers, for instance, it seems very important to know, how much attention commercial attracts. In this article, I will describe attention detector module, used in our Audience Measurement system.
Posted on : 18-06-2009 | By : rhondasw | In : OpenCV
In OpenCV/Samples there is facedetect program. This program can detect faces on images and video. It’s very fun, but its speed leaves much to be desired =(. Of course with OpenMP, it works faster; on Intel Core Duo 2.7GHZ, it works fast; but will it work fast on ARM? I have big doubts. I compiled facedetect without OpenMP and on average it takes 600 ms for 640×480 resolution to find one face. I wanted to find out, if it’s possible to improve this time by software means or not… After some investigations, code refactoring and improvements, facedetect started to work 2.5 time faster, even on ARM. Of course, without big quality loss =)