barcodes.avi

Description

Here is a demo of the jerry-built algorithm that finds barcode plates using Hough transform. Actually the video is mostly speaking for itself. Two points to comment:

• The frames of barcodes are a bit long, since with given approach there is distinct difficulty in precise identification of barcode beginning and ending, so borders where widened to not miss the useful part, which is not really critical since extracted region of interest is still quite small.
• The task was to detect only one barcode, so when there are several of them in the camera sight, CV system selects the best one (those with the most distinct lines). Since conditions of lighting and sharpness are always floating in video, system jumps from one barcode to another.

PTZ_800x600_sonicfire.avi

Description

This demo video demonstrates ability of CV system to take snapshots of several moving people by means of two digital cameras: static (QuickCam Pro 9000) and moving (AXIS 214) PTZ (pan/tilt/zoom). CV system tracks moving people (using color-histogram-based tracker) on the video taken by the static camera and targeting PTZ cam to one of people, negotiating the number of already captured snapshots per person and distance to peoples on scene. Since PTZ cam positioning takes some time, the predicting algorithm is used to forecast future position of the person, which allows targeting PTZ cam more accurately.

cards_800x600_sonicfire.avi

Description

This is a demo of object recognition technique based on extraction and matching of characteristic points on objects with evident texture. This technique allows recognizing object by various angles and in case of partial occlusions. The demo clip is self-explanatory – obviously it works just fine.

Today’s story is about improving performance of OpenCV library on the ARM-based platforms.

As you already know (from here or from here or may be even from here), face detection algorithm implemented by OpenCV library doesn’t work perfectly on ARM processors. Science doesn’t know for certain why this happens. There might be several possible reasons. One of our assumption was missing of hardware support for floating point operations. So we tried to translate Viola-Jones algorithm from floating point to fixed point. And that’s how we did this…

By default OpenCV 1.1 don’t support AXIS ip-cameras. So, this paper describes approach for getting camera interface (cvCaptureFromCAM) from OpenCV when you are using an Axis Ip camera.

This time OpenCV was ported to the Apple iPhone platform.

First of all we need to compile OpenCV library itself so that it can be used on the iPhone. There are two ways here:

1. Use OpenCV as a private framework.
2. Compile OpenCV as a static library.

First approach looks more comfortable for using, though I was not able to make it work properly on the iPhone (it works fine on the simulator, but not on the real hardware).

But anyway, let’s see how both approaches can be followed.