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