Path Relinking Particle Filter for Human Body Pose Estimation

Pantrigo, Juan José; Sánchez, Ángel Mediavilla; Gianikellis, Kostas; Duarte, Abraham

doi:10.1007/978-3-540-27868-9_71

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…On the other hand, the MA improves the quality of the particle set, allowing a better estimation of the pdf, by including the Im-provedSet solutions in the SupportSet. These facts offer general and highly configurable algorithms suited to the particular dynamic optimization problem being solved, as evidenced in Section 5 and previous related work (Pantrigo et al, 2004(Pantrigo et al, , 2005(Pantrigo et al, , 2008Cabido et al, in press).…”

Section: Selection Estimation and Replacement Proceduresmentioning

confidence: 87%

“…In our MAPF algorithm, all the solutions in the ImprovedSet replace those in the SupportSet. Other implementations of these procedures can be found in Pantrigo et al (2004, 2008).…”

Section: Case Study: Memetic Algorithm Particle Filtermentioning

confidence: 99%

“…There are also several algorithms based on the hybridization of PF and genetic algorithms (Cui & Sun, 2004a, b; Deutscher & Reid, 2005). In Pantrigo et al (2004), the path relinking PF was proposed and applied to an articulated motion tracking problem. In Pantrigo et al (2008) the scatter search PF was applied to multidimensional visual tracking problems.…”

Section: Introductionmentioning

confidence: 99%

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Heuristic particle filter: applying abstraction techniques to the design of visual tracking algorithms

2010

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Many real-world visual tracking applications have a high dimensionality, i.e. the system state is defined by a large number of variables. This kind of problem can be modelled as a dynamic optimization problem, which involves dynamic variables whose values change in time. Most applied research on optimization methods have focused on static optimization problems but these static methods often lack explicit adaptive methodologies. Heuristics are specific methods for solving problems in the absence of an algorithm for formal proof. Metaheuristics are approximate optimization methods which have been applied to more general problems with significant success. However, particle filters are Monte Carlo algorithms which solve the sequential estimation problem by approximating the theoretical distributions in the state space by simulated random measures called particles. However, particle filters lack efficient search strategies. In this paper, we propose a general framework to hybridize heuristics=metaheuristics with particle filters properly. The aim of this framework is to devise effective hybrid visual tracking algorithms naturally, guided by the use of abstraction techniques. Resulting algorithms exploit the benefits of both complementary approaches. As a particular example, a memetic algorithm particle filter is derived from the proposed hybridization framework. Finally, we show the performance of the memetic algorithm particle filter when it is applied to a multiple object tracking problem.

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Section: Selection Estimation and Replacement Proceduresmentioning

confidence: 87%

“…In our MAPF algorithm, all the solutions in the ImprovedSet replace those in the SupportSet. Other implementations of these procedures can be found in Pantrigo et al (2004, 2008).…”

Section: Case Study: Memetic Algorithm Particle Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Heuristic particle filter: applying abstraction techniques to the design of visual tracking algorithms

2010

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“…Finally, particle set at time step t + 1, X t+1 , is predicted using the motion model. A pseudocode of a general PF is detailed in [1] [17].…”

Section: Particle Filtersmentioning

confidence: 99%

“…Path Relinking Particle Filter (PRPF) algorithm was introduced in [17] to be applied to estimation problems in sequential processes that can be expressed using the state-space model abstraction. PRPF integrates both Path Relinking (PR) and Particle Filter (PF) frameworks in two different stages: • In the Particle Filter stage, a particle (solution) set is propagated over the time and updated with measurements to obtain a new one.…”

Section: Path Relinking Particle Filtermentioning

confidence: 99%

Combining Particle Filter and Population-Based Metaheuristics for Visual Articulated Motion Tracking

Pantrigo¹,

Sánchez²,

Montemayor³

et al. 2009

Series in Machine Perception and Artificial Intelligence

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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 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 the proposed algorithms increase the performance of standard particle filters.

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2D Human Tracking by Efficient Model Fitting Using a Path Relinking Particle Filter

Pantrigo

Sánchez

Gianikellis

et al. 2004

Articulated Motion and Deformable Objects

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Path Relinking Particle Filter for Human Body Pose Estimation

Cited by 8 publications

References 12 publications

Heuristic particle filter: applying abstraction techniques to the design of visual tracking algorithms

Heuristic particle filter: applying abstraction techniques to the design of visual tracking algorithms

Combining Particle Filter and Population-Based Metaheuristics for Visual Articulated Motion Tracking

2D Human Tracking by Efficient Model Fitting Using a Path Relinking Particle Filter

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