期刊名称：ELCVIA: electronic letters on computer vision and image analysis
印刷版ISSN：1577-5097
出版年度：2005
卷号：5
期号：3
页码：68-83
DOI：10.5565/rev/elcvia.107
出版社：Centre de Visió per Computador
摘要：Visual tracking of articulated motion is a complex task with high computational costs. Because of the fact that articulated objects are usually represented as a set of linked limbs, tracking is performed with the support of a model. Model-based tracking allows determining object pose in an effortless way and handling occlusions. However, the use of articulated models generates a multidimensional state-space and, therefore, the tracking becomes computationally very expensive or even infeasible. Due to the dynamic nature of the problem, some sequential estimation algorithms like particle filters are usually applied to visual tracking. Unfortunately, particle filter fails in high dimensional estimation problems such as articulated objects or multiple object tracking. These problems are called \emph{dynamic optimization problems}. Metaheuristics, which are high level general strategies for designing heuristics procedures, have emerged for solving many real world combinatorial problems as a way to efficiently and effectively exploring the problem search space. Path relinking (PR) and scatter search (SS) are evolutionary metaheuristics successfully applied to several hard optimization problems. PRPF and SSPF algorithms respectively hybridize both, particle filter and these two population-based metaheuristic schemes. In this paper, We present and compare two different hybrid algorithms called Path Relinking Particle Filter (PRPF) and Scatter Search Particle Filter (SSPF), applied to 2D human motion tracking. Experimental results show that the proposed algorithms increase the performance of standard particle filters.
其他摘要：Visual tracking of articulated motion is a complex task with high computational costs. Because of the fact that articulated objects are usually represented as a set of linked limbs, tracking is performed with the support of a model. Model-based tracking allows determining object pose in an effortless way and handling occlusions. However, the use of articulated models generates a multidimensional state-space and, therefore, the tracking becomes computationally very expensive or even infeasible. Due to the dynamic nature of the problem, some sequential estimation algorithms like particle filters are usually applied to visual tracking. Unfortunately, particle filter fails in high dimensional estimation problems such as articulated objects or multiple object tracking. These problems are called \emph{dynamic optimization problems}. Metaheuristics, which are high level general strategies for designing heuristics procedures, have emerged for solving many real world combinatorial problems as a way to efficiently and effectively exploring the problem search space. Path relinking (PR) and scatter search (SS) are evolutionary metaheuristics successfully applied to several hard optimization problems. PRPF and SSPF algorithms respectively hybridize both, particle filter and these two population-based metaheuristic schemes. In this paper, We present and compare two different hybrid algorithms called Path Relinking Particle Filter (PRPF) and Scatter Search Particle Filter (SSPF), applied to 2D human motion tracking. Experimental results show that the proposed algorithms increase the performance of standard particle filters. keywords: Articulated Motion Tracking, Particle Filters, Population-based Metaheuristics
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