Feature-driven motion model-based particle-filter tracking method with abrupt motion handling

The potential for the research of object tracking in computer vision has been well established, but previous object-tracking methods, which consider only continuous and smooth motion, are limited in handling abrupt motions. We introduce an efficient algorithm to tackle this limitation. A feature-driven (FD) motion model-based features from accelerated segment test (FAST) feature matching is proposed in the particle-filtering framework. Various evaluations have demonstrated that this motion model can improve existing methods' performances to handle abrupt motion significantly. The proposed model can be applied to most existing particle-filter tracking methods. (C) 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.OE.51.4.047203]