Three-dimensional pose tracking by image correlation and particle filtering

Picos, Kenia; Díaz-Ramírez, Víctor H.; Montemayor, Antonio S.; Pantrigo, Juan José; Kober, Vitaly

doi:10.1117/1.oe.57.7.073108

Cited by 9 publications

(9 citation statements)

References 36 publications

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“…Particle filtering is widely employed in object pose estimation problems, where the overall objective is to estimate pose of a moving object from a collection of samples arriving sequentially. Particle filters can be seen as population-based Monte Carlo algorithms, in which the distribution of the pose space is approximated by random measures, called particles [11]. Each particle is composed of a single pose and has an associated weighting coefficient.…”

Section: Objective Function For 2d-3d Object Pose Estimation Using CImentioning

confidence: 99%

“…To estimate the pose of the object with particle filters, the particles q k i are selected according to their weights [11], determined by the fitness function of Eq. 5, in which the particles with more probability to be selected are those that contain higher weight values.…”

Section: Objective Function For 2d-3d Object Pose Estimation Using CImentioning

confidence: 99%

“…So, they do not exploit the underlying dynamics inherent in a pose estimation task and they cannot handle erratic movements. In [2] and [11], not only to utilize both framework above but also to overcome their disadvantages, they extend them by incorporating a particle filter to exploit the underlying dynamics of system; this improvement provides the robust method for object 3D pose estimation in the presence of additive noise, complex background, and occlusion. Both methods rely on the 3D model of the object to obtain prior information and construct a 6D pose parameter model to estimate the object pose.…”

Section: Introductionmentioning

confidence: 99%

“…In Section 2, we define the representation method of the 5D circle pose parameters and give the objective function using the maximum posterior probability estimation (MAP) for 2D-3D object pose estimation using circular feature. In addition, we briefly explain an overview of the fundamental concepts used in the proposed method, particle filters presented in [11]. Section 3 explains the proposed algorithm for 2D-3D object pose estimation using circular feature.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Elliptic shape prior for object 2D-3D pose estimation using circular feature

Cui

Chen

Gong

et al. 2020

EURASIP J. Adv. Signal Process.

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Many objects in real world have circular feature. It is a difficult task to obtain the 2D-3D pose estimation using circular feature in challenging scenarios. This paper proposes a method to incorporate elliptic shape prior for object pose estimation using a level set method. The relationship between the projection of the circular feature of a 3D object and the signed distance function corresponding to it is analyzed to yield a 2D elliptic shape prior. The method employs the combination of the grayscale histogram, the intensities of edge, and the smoothness distribution as main image feature descriptors that define the image statistical measure model. The elliptic shape prior combined with the image statistical measure energy model drives the elliptic shape contour to the projection of the circular feature of the 3D object with the current pose into the image plane. These works effectively reduce the impacts of the challenging scenarios on the pose estimate results. In addition, the method utilizes particle filters that take into account the motion dynamics of the object among scene frames, and this work provides the robust method for object 2D-3D pose estimation using circular feature in a challenging environment. Various numerical experiments are illustrated to show the performance and advantages of the proposed method.

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Section: Objective Function For 2d-3d Object Pose Estimation Using CImentioning

confidence: 99%

Section: Objective Function For 2d-3d Object Pose Estimation Using CImentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Elliptic shape prior for object 2D-3D pose estimation using circular feature

Cui

Chen

Gong

et al. 2020

EURASIP J. Adv. Signal Process.

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show abstract

“…Moreover, the pose estimation problem can be modeled as a search problem, in which the goal is to find the reference target view that gives the best match between the actual view of the target in the scene [14]. By using a template matching 2 Mathematical Problems in Engineering approach, a big set of correlation filters can be required to find a good match [15]. The design and construction of correlation filters is a high-dimensional complex problem, thus finding an optimal solution will require a very large search space.…”

Section: Introductionmentioning

confidence: 99%

Pose Estimation in Noncontinuous Video Sequences Using Evolutionary Correlation Filtering

Picos

Orozco-Rosas

Díaz-Ramírez

et al. 2018

Mathematical Problems in Engineering

Self Cite

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In this paper, we propose an evolutionary correlation filtering approach for solving pose estimation in noncontinuous video sequences. The proposed algorithm computes the linear correlation between the input scene containing a target in an unknown environment and a bank of matched filters constructed from multiple views of the target and estimates of statistical parameters of the scene. An evolutionary approach for finding the optimal filter that produces the highest matching score in the correlator is implemented. The parameters of the filter bank evolve through generations to refine the quality of pose estimation. The obtained results demonstrate the robustness of the proposed algorithm in challenging image conditions such as noise, cluttered background, abrupt pose changes, and motion blur. The performance of the proposed algorithm yields high accuracy in terms of objective metrics for pose estimation in noncontinuous video sequences.

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Evolutionary correlation filtering based on pseudo-bacterial genetic algorithm for pose estimation of highly occluded targets

Picos

Orozco-Rosas

2020

Multimed Tools Appl

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Three-dimensional pose tracking by image correlation and particle filtering

Cited by 9 publications

References 36 publications

Elliptic shape prior for object 2D-3D pose estimation using circular feature

Elliptic shape prior for object 2D-3D pose estimation using circular feature

Pose Estimation in Noncontinuous Video Sequences Using Evolutionary Correlation Filtering

Evolutionary correlation filtering based on pseudo-bacterial genetic algorithm for pose estimation of highly occluded targets

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