Advances in real-time object tracking: Extensions for robust object tracking with a Monte Carlo particle filter.

ABSTRACT: The huge amount of literature on real-time object tracking continuously reports good results with respect to accuracy and robustness. However, when it comes to the applicability of these approaches to real-world problems, often no clear statements about the tracking situation can be made. This paper addresses this issue and relies on three novel extensions to Monte Carlo particle filtering. The first, confidence dependent variation, together with the second, iterative particle filtering, leads to faster convergence and a more accurate pose estimation. The third, fixed particle poses removes jitter and ensures convergence. These extensions significantly increase robustness and accuracy, and further provide a basis for an algorithm we found to be essential for tracking s

SUBMITTER: Morwald T 

PROVIDER: S-EPMC7089693 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature