Polynomial fitting adaptive Kalman filter tracking and choice of correlation coefficient

Kyle Ausfeld; Zoran Ninkov; Paul P. K. Lee; J. Daniel Newman; Gregory Gosian

doi:10.1117/12.919717

Translator Disclaimer

15 May 2012 Polynomial fitting adaptive Kalman filter tracking and choice of correlation coefficient

Kyle Ausfeld, Zoran Ninkov, Paul P. K. Lee, J. Daniel Newman, Gregory Gosian

Author Affiliations +

Proceedings Volume 8395, Acquisition, Tracking, Pointing, and Laser Systems Technologies XXVI; 83950R (2012) https://doi.org/10.1117/12.919717
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

Kalman filters have been used as a robust method for object location prediction in various tracking algorithms for nearly a decade. More recently, adaptive and extended Kalman filters have been employed, making predictions even more reliable. The presented addition to this trend is the employment of a polynomial fit to the history of object locations, using the adaptive Kalman filter framework. This allows the linear state model of the adaptive Kalman filter to predict non-linear motion, making tracking more robust. This modified filter will be used in conjunction with the Mean Shift algorithm as the measurement step. Another important consideration when using a Kalman filter in this manner will be which correlation coefficient is used. The Pearson product-moment correlation coefficient is shown to provide more robust tracking when compared to the Bhattacharyya coefficient when objects have either low resolution or are unresolved.

Citation Download Citation

Kyle Ausfeld, Zoran Ninkov, Paul P. K. Lee, J. Daniel Newman, and Gregory Gosian "Polynomial fitting adaptive Kalman filter tracking and choice of correlation coefficient", Proc. SPIE 8395, Acquisition, Tracking, Pointing, and Laser Systems Technologies XXVI, 83950R (15 May 2012); https://doi.org/10.1117/12.919717

ACCESS THE FULL ARTICLE