Particle filter has attracted much attention due to its robust tracking performance in clutter. However, a price to pay for its robustness is the computational cost. Meanwhile there is no exact mechanism for choosing or updating scale in its framework for accurate tracking. In this paper we propose a threshold and scale based particle filter (TSPF). It employs a threshold to discard the bad particles and keep the good ones. In this case, the efficiency of particles is improved and the number of required particles is greatly reduced. It also adapts Robert T. Collins's theory of selecting kernel scale for mean shift blob tracking to particle filter. Experiments show TSPF works well, both spatially and in scale.

Date Published: 15 November 2007
PDF: 8 pages
Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 678631 (15 November 2007); doi: 10.1117/12.749774

Published in SPIE Proceedings Vol. 6786:
MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition
Tianxu Zhang; Tianxu Zhang; Carl Anthony Nardell; Carl Anthony Nardell; Hanqing Lu; Duane D. Smith; Hangqing Lu, Editor(s)