Highest quality full cycle software development.
Expert in areas of Computer Vision, Multimedia, Messaging, Networking and others. Focused on embedded software development. Competent in building cross-platform solutions and distributed SW systems.
Offer standalone custom solutions as well as integration of existing products. Opened for outsourcing services.
Visit us at: http://www.rhondasoftware.com
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).
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).
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.
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.
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.
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 🙂
This is a demo video of Rhonda Audience Measurement system (MyAudience product, www.MyAudience.com).
This paper describes how-to get MJPEG stream from AXIS ip-camera in your C++ application. My approach is a cross-platform solution and much better than solution from http://www.computer-vision-software.com/blog/2009/04/how-to-get-mjpeg-stream-from-axis-ip-cameras-axis-211m-and-axis-214-ptz-as-camera-device-in-opencv-using-directshow/.
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 =)
If you want to generate cascade with OpenCV training tools, you should be ready for waiting plenty of time. For example, on training set: 3000 positive / 5000 negative, it takes about 6 days! to get cascade for face detection. I wanted to generate many cascades with different training sets, also I added my own features to standart OpenCV’s ones and refactor algorithms a little bit. So waiting for 6 days to understand, that your cascade does nothing good =) was really anoying. To reduce time, I chose paralleling methods.
