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

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

am-3.avi

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

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).

overlapped_sonicfire.avi

Description

This video demo illustrates color-histogram-based object tracker in action. CV system tracks people as moving blobs (“clouds” of moving pixels) identifies them and distinct one from another in case of occlusions. When two (or more) blobs are intersected, system merges them in one combined object and marks it by IDs of all those source-objects that currently included in the combination. When one of objects separates from the combination CV system recognize which one is out and re-arrange ID appropriately. This approach works pretty well in case of characteristic histograms.

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.