I am trying to calculate the trajectory of a moving object (specifically, a thrown object) through a series of video frames. My tracking algorithm can reliably detect ~90% of the object occurrences throughout the video, giving an x, y, and radius size (in pixels) value. However, the tracking method is not resilient to noise and therefore a lot of false-positives are picked up too.

I was wondering if anyone could point me in the right direction to finding an algorithm that will help me to:

  • (1) Filter the noisy data from the found points
  • (2) build a good trajectory estimation using these filtered points and a simple physics model
  • (3) potentially even help to interpolate points within the trajectory that were missed by the tracking algorithm.

Any help would be greatly appreciated.


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