A Novel Object Tracking Algorithm using Kalman Filter with Adaptive Prediction
Hamidreza Rabiee, Javad Haddadnia, Omid Rahmani Seryasat · 2013
Our method tracks the changing rate of the transform parameters and makes prediction on future values of the transform parameters to determine the initial searching point. More importantly, noises in the Kalman filter are effectively estimated in our approach without any artificial assumption, which makes our method able to adapt to various target motions and searching step sizes without any manual intervention. Simulation results demonstrate the effectiveness of our algorithm. With a dynamic measurement error covariance computed from these estimates, we attempt to produce an overall object tracking filter that combines each algorithm's best-case behavior while diminishing worst-case behavior. This filter is intended to be robust without being programmed with any environment-specific rules. (Hamidreza Rabiee, Javad Haddadnia, Omid Rahmani Seryasat. A Novel Object Tracking Algorithm using Kalman Filter with Adaptive Prediction. J Am Sci 2013;9(2s):137-142). (ISSN: 1545-1003). http://www.jofamericanscience.org. 21