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 | 1 | initial version |
Hi mikos,
do you detect the person with a detector (e.g. HoG Descriptor or Haar Classifier) ? Then you can use a tracking algorithm to track the detected object boundaries (or the center of the boundaries). A good introduction into Object Tracking can be found here.
OpenCV contains an implementation of the Kalman Filter see here. and here. The Kalman filter is widely used to track object. But to detect persons the definition of a proper motion model is hard.
An other easier filter for tracking is the alpha-beta filter. This filter is very easy to develop (only some matrix operations) and doesn't need a motion model.
Both filters can handle move-stop-move events. If you have to detect more than one person in the image you have to start for each detected person an separate tracker instance. The assignment of the detected person to the right tracker could then be hard.
| | 2 | No.2 Revision |
Hi mikos,
do you detect the person with a detector (e.g. HoG Descriptor or Haar Classifier) ? Then you can use a tracking algorithm to track the detected object boundaries (or the center of the boundaries). A good introduction into Object Tracking can be found here.
OpenCV contains an implementation of the Kalman Filter
see here. and here. The Kalman filter is widely used to track object. But to detect persons the definition of a proper motion model is hard.
An other easier filter for tracking is the alpha-beta filter. This filter is very easy to develop (only some matrix operations) and doesn't need a motion model.
Both filters can handle move-stop-move events. If you have to detect more than one person in the image you have to start for each detected person an separate tracker instance. The assignment of the detected person to the right tracker could then be hard.
