Abstract
Template matching and updates are crucial steps in visual object tracking. In this article, we propose a two-stage object tracking algorithm using a dual-template. By design, the initial state of a target can be estimated using a prior fixed template at the first stage with a particle-filter-based tracking framework. The use of prior templates maintains the stability of an object tracking algorithm, because it consists of invariant and important features. In the second step, a mean shift is used to gain the optimal location of the object with the stage update template. The stage template improves the ability of target recognition using a classified update method. The complementary of dual-template improves the quality of template matching and the performance of object tracking. Experimental results demonstrate that the proposed algorithm improves the tracking performance in terms of accuracy and robustness, and it exhibits good results in the presence of deformation, noise and occlusion.
Highlights
Object detection and tracking are prerequisites for a number of practical applications in computer and robotic vision.[1,2,3,4,5] In recent years, particle filter (PF) has become one of the most popular visual tracking methods, due to its outstanding performance in optimal estimation problems of non-linear and nonGaussian systems.[6,7,8,9] The PF-based method is usually used to tackle complicated tracking problems using model matching
The first step is the initial target location using a prior fixed template, and the second step is accurate target matching by a stage template update
The mean shift algorithm is embedded to the particle filter algorithm
Summary
Object detection and tracking are prerequisites for a number of practical applications in computer and robotic vision.[1,2,3,4,5] In recent years, particle filter (PF) has become one of the most popular visual tracking methods, due to its outstanding performance in optimal estimation problems of non-linear and nonGaussian systems.[6,7,8,9] The PF-based method is usually used to tackle complicated tracking problems using model matching. Keywords Visual tracking, particle filter, mean shift, prior fixed template, stage update template In order to improve the target matching efficiency, a fusion method with a prior fixed template and a stage update template is introduced.
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