Visual Tracking via Particle Filtering on the Affine Group

Junghyun, Kwon; Park, Frank C.

doi:10.1177/0278364909345167

Cited by 40 publications

(57 citation statements)

References 31 publications

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“…For this purpose, we developed a novel method for tracking in depth images based on a geometric particle filter on the affine group. This type of tracking paradigm has been used before in color images [7], [22]. An advantage of our method compared to color-based tracking is that its performance is independent of the appearance of the object in terms of color and texture (see Fig.…”

Section: Discussionmentioning

confidence: 99%

“…The surface template I t=0 (P) is periodically updated every five frames by computing the mean of the 3D shape that has been tracked during this interval. Detailed description for calculating the measurement likelihood p(y t |X t ) for the importance sampling step as well as the particle resampling step can be found in [7], [22].…”

Section: Related Workmentioning

confidence: 99%

“…However, extensive experimental results with non-planar surfaces have revealed that under weak-perspective assumption such a transformation can be approximated with a two-dimensional affine transform in the image plane. Previously, this assumption has been made in [22], [23], [24], [25] for color/grayscale images.…”

Section: Related Workmentioning

confidence: 99%

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Realtime tracking and grasping of a moving object from range video

Husain

Colomé

Dellen

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-In this paper we present an automated system that is able to track and grasp a moving object within the workspace of a manipulator using range images acquired with a Microsoft Kinect sensor. Realtime tracking is achieved by a geometric particle filter on the affine group. Based on the tracked output, the pose of a 7-DoF WAM robotic arm is continuously updated using dynamic motor primitives until a distance measure between the tracked object and the gripper mounted on the arm is below a threshold. Then, it closes its three fingers and grasps the object. The tracker works in realtime and is robust to noise and partial occlusions. Using only the depth data makes our tracker independent of texture which is one of the key design goals in our approach. An experimental evaluation is provided along with a comparison of the proposed tracker with state-of-the-art approaches, including the OpenNItracker. The developed system is integrated with ROS and made available as part of IRI's ROS stack.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Realtime tracking and grasping of a moving object from range video

Husain

Colomé

Dellen

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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“…The filter which is most commonly used for video object tracking in the literature [15], [23], [16] is the Sampling Importance Resampling (SIR) filter proposed by [14].…”

Section: Color-based Particle Filtersmentioning

confidence: 99%

Region-Based Particle Filter for Video Object Segmentation

Varas

Marqués

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

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We present a video object segmentation approach that extends the particle filter to a region-based image representation. Image partition is considered part of the particle filter measurement, which enriches the available information and leads to a re-formulation of the particle filter. The prediction step uses a co-clustering between the previous image object partition and a partition of the current one, which allows us to tackle the evolution of non-rigid structures. Particles are defined as unions of regions in the current image partition and their propagation is computed through a single co-clustering. The proposed technique is assessed on the SegTrack dataset, leading to satisfactory perceptual results and obtaining very competitive pixel error rates compared with the state-of-the-art methods.

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“…Yet, this is computationally infeasible. In theory, the particle filter can track any parametric variation including the pose as in [8] where the affine motion is imposed as the state and particle filtering is applied on affine group. However, the intrinsic dependency to random sampling tends to degenerate and debilitate the estimated likelihoods especially for higher dimensional pose representations.…”

Section: Introductionmentioning

confidence: 99%

Regressed Importance Sampling on Manifolds for Efficient Object Tracking

Porikli

Pan

2009

2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance

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In this paper, a new integrated particle filter is proposed for video object tracking. After particles are generated by importance sampling, each particle is regressed on the transformation space where the mapping function is learned offline by regression on pose manifold using Lie algebra, leading to a more effective allocation of particles. Experimental results on synthetic and real sequences clearly demonstrated the improved pose (affine) tracking performance of the proposed method compared with the original regression tracker and particle filters. AVSS 2009This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. AbstractIn this paper, a new integrated particle filter is proposed for video object tracking. After particles are generated by importance sampling, each particle is regressed on the transformation space where the mapping function is learned offline by regression on pose manifold using Lie algebra, leading to a more effective allocation of particles. Experimental results on synthetic and real sequences clearly demonstrate the improved pose (affine) tracking performance of the proposed method compared with the original regression tracker and particle filters.

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Visual Tracking via Particle Filtering on the Affine Group

Cited by 40 publications

References 31 publications

Realtime tracking and grasping of a moving object from range video

Realtime tracking and grasping of a moving object from range video

Region-Based Particle Filter for Video Object Segmentation

Regressed Importance Sampling on Manifolds for Efficient Object Tracking

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