More Web Proxy on the site http://driver.im/

article

Robust edge-based 3D object tracking with direction-based pose validation

Authors:

Xueying QinAuthors Info & Claims

Multimedia Tools and Applications, Volume 78, Issue 9

Pages 12307 - 12331

https://doi.org/10.1007/s11042-018-6727-5

Published: 01 May 2019 Publication History

Abstract

In this paper we propose a robust edge-based approach for 3D textureless object tracking. We first introduce an edge-based pose estimation method, which minimizes the holistic distance between the projected object contour and the query image edges, without explicitly searching for 3D-2D correspondences. This method is accurate with a good initialization; however, it is sensitive to occlusion and fast motion, thus often gets lost in real environments. To improve robustness, we exploit consistency of edge direction for validating the correctness of the estimated 3D pose, and further incorporate the validation scheme for robust estimation, non-local searching and failure recovery. The robust estimation adopts point-wise validation to reduce the effect of outlier, resulting in a direction-based robust estimator. The non-local searching is based on particle filter, with the pose validation for a faithful weighting of particles, which is shown to be better than the distance-based weighting. The failure recovery is based on fast 2D detection, and estimates the recovered pose by searching for 3D-2D point correspondences, with the validation scheme to adaptively determine state transition. The effectiveness of our approach is demonstrated using comparative experiments on real image sequences with occlusions, large motions and background clutters.

References

[1]

Besl PJ, Mckay ND (1992) Method for registration of 3-D shapes. In: Robotics - DL tentative, pp 239---256

Digital Library

[2]

Calonder M, Lepetit V, Strecha C, Fua P (2010) BRIEF: Binary robust independent elementary features. In: European conference on computer vision, pp 778---792

Digital Library

[3]

Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679---98

Digital Library

[4]

Chen Y, Medioni G (1992) Object modelling by registration of multiple range images. Image Vis Comput 10(3):145---155

Digital Library

[5]

Choi C, Christensen HI (2012) 3D textureless object detection and tracking: an edge-based approach. In: International conference on intelligent robotics and system, pp 3877---3884

[6]

Choi C, Christensen HI (2012) Robust 3D visual tracking using particle filtering on the special Euclidean group: A combined approach of keypoint and edge features. Int J Robot Res 33(4):498---519

Digital Library

[7]

Comport AI, Marchand E, Chaumette F (2003) A real-time tracker for markerless augmented reality. In: IEEE International symposium on mixed and augmented reality, pp 36---45

Digital Library

[8]

Felzenszwalb PF, Huttenlocher DP (2004) Distance transforms of sampled functions. Theory of Computing 8(19):415---428

[9]

Fitzgibbon AW (2003) Robust registration of 2D and 3D point sets. Image Vis Comput 21(13):1145---1153

[10]

Gao X, Hou XR, Tang J, Cheng HF (2003) Complete solution classification for the perspective-three-point problem. IEEE Trans Pattern Anal Mach Intell 25 (8):930---943

Digital Library

[11]

Harris C, Stennett C (1990) RAPId - a video-rate object tracker. In: British machine vision conference, pp 73---77

[12]

Hartley R, Zisserman A (2000) Multiple view geometry in computer vision. Cambridge University Press

Digital Library

[13]

Hinterstoisser S, Cagniart C, Ilic S, Sturm P, Navab N, Fua P, Lepetit V (2012) Gradient response maps for real-time detection of textureless objects. IEEE Trans Pattern Anal Mach Intell 34(5):876---888

Digital Library

[14]

Hinterstoisser S, Lepetit V, Ilic S, Fua P, Navab N (2010) Dominant orientation templates for real-time detection of texture-less objects. In: IEEE Conference on computer vision and pattern recognition, pp 2257---2264

[15]

Hinterstoisser S, Lepetit V, Ilic S, Holzer S, Bradski G, Konolige K, Navab N (2012) Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes. In: Asian conference on computer vision, pp 548---562

Digital Library

[16]

Imperoli M, Pretto A (2015) D2CO: Fast and robust registration of 3D textureless objects using the directional chamfer distance. In: International conference on computer vision systems, pp 316---328

Digital Library

[17]

Isard M, Blake A (1998) CONDENSATION - Conditional density propagation for visual tracking. In: International journal of computer vision, pp 5---28

Digital Library

[18]

Kato H, Billinghurst M (1999) Marker tracking and HMD calibration for a video-based augmented reality conferencing system. In: IEEE And ACM international workshop on augmented reality, pp 85---94

Digital Library

[19]

Klein G, Murray DW (2006) Full-3D edge tracking with a particle filter. In: British machine vision conference, pp 1119---1128

[20]

Lepetit V, Fua P (2005) Monocular model-based 3D tracking of rigid objects: a survey. Found Trends Comput Graph Vis 1(1):1---89

Digital Library

[21]

Lepetit V, Moreno-Noguer F (2009) Epnp: an accurate o(n) solution to the pnp problem. Int J Comput Vis 81(2):155---166

Digital Library

[22]

Liu MY, Tuzel O, Veeraraghavan A, Chellappa R (2010) Fast directional chamfer matching. In: IEEE Conference on computer vision and pattern recognition, pp 1696---1703

[23]

Lourakis M, Zabulis X (2013) Model-based pose estimation for rigid objects. In: International conference on computer vision systems, pp 83---92

Digital Library

[24]

Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91---110

Digital Library

[25]

Marchand E, Bouthemy P, Chaumette F (2001) A 2Dc3D model-based approach to real-time visual tracking. Image Vis Comput 19(13):941---955

[26]

Marchand E, Uchiyama H, Spindler F (2015) Pose estimation for augmented reality: a hands-on survey. IEEE Trans Vis Comput Graph 22(12):2633---2651

Digital Library

[27]

Park Y, Lepetit V, Woo W (2008) Multiple 3D object tracking for augmented reality. In: IEEE International symposium on mixed and augmented reality, pp 117---120

Digital Library

[28]

Park Y, Lepetit V, Woo W (2011) Texture-less object tracking with online training using an rgb-d camera. In: IEEE International symposium on mixed and augmented reality, pp 121---126

Digital Library

[29]

Prisacariu VA, Reid ID (2012) PWP3D: Real-time segmentation and tracking of 3d objects. Int J Comput Vis 98(3):335---354

Digital Library

[30]

Rublee E, Rabaud V, Konolige K, Bradski G (2011) ORB: An efficient alternative to SIFT or SURF. In: IEEE International conference on computer vision, pp 2564---2571

Digital Library

[31]

Seo BK, Park H, Park JI, Hinterstoisser S, Ilic S (2014) Optimal local searching for fast and robust textureless 3D object tracking in highly cluttered backgrounds. IEEE Trans Vis Comput Graph 20(1):99---110

Digital Library

[32]

Shotton J, Glocker B, Zach C, Izadi S, Criminisi A, Fitzgibbon A (2013) Scene coordinate regression forests for camera relocalization in rgb-d images. In: IEEE Conference on computer vision and pattern recognition, pp 2930---2937

Digital Library

[33]

Vacchetti L, Lepetit V, Fua P (2004) Combining edge and texture information for real-time accurate 3D camera tracking. In: IEEE International symposium on mixed and augmented reality, pp 48---56

Digital Library

[34]

Vacchetti L, Lepetit V, Fua P (2004) Stable real-time 3D tracking using online and offline information. IEEE Trans Pattern Anal Mach Intell 26(10):1385---1391

Digital Library

[35]

Wang B, Zhong F, Qin X (2017) Pose optimization in edge distance field for textureless 3d object tracking. In: The computer graphics international conference, pp 1---6

Digital Library

[36]

Wang G, Wang B, Zhong F, Qin X, Chen B (2015) Global optimal searching for textureless 3D object tracking. Vis Comput 31(6):979---988

Digital Library

[37]

Wuest H, Vial F, Strieker D (2005) Adaptive line tracking with multiple hypotheses for augmented reality. In: IEEE International symposium on mixed and augmented reality, pp 62---69

Digital Library

Cited By

Li WWang JWei M(2024)Local geometric edge features based registration for textureless object in augmented reality assisted assemblyVirtual Reality10.1007/s10055-023-00922-928:1Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1007/s10055-023-00922-9
Li JZhong FXu SQin X(2021)3D Object Tracking with Adaptively Weighted Local BundlesJournal of Computer Science and Technology10.1007/s11390-021-1272-536:3(555-571)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s11390-021-1272-5
Zhou DChai ZLiu CLi PXiong Z(2020)6D Pose Estimation for Texture-Less Industrial Parts in the CrowdIntelligent Robotics and Applications10.1007/978-3-030-66645-3_35(415-428)Online publication date: 5-Nov-2020
https://dl.acm.org/doi/10.1007/978-3-030-66645-3_35

Robust edge-based 3D object tracking with direction-based pose validation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks

Recommendations

Pose optimization in edge distance field for textureless 3D object tracking
CGI '17: Proceedings of the Computer Graphics International Conference

This paper presents a monocular model-based 3D tracking approach for textureless objects. Instead of explicitly searching for 3D-2D correspondences as previous methods, which unavoidably generates individual outlier matches, we aim to minimize the ...
Robust multi-camera 3D tracking from mono-camera 2d tracking using Bayesian Association

Visual tracking is essential for automatic scene understanding and surveillance of areas of interest. Monocular 2D tracking has been largely studied in the literature, but it usually provides inadequate or incomplete information for event ...
Towards Automatic 3D Pose Tracking through Polygon Mesh Approximation
Advances in Artificial Intelligence – IBERAMIA 2012
Abstract
A method for visual 3D pose tracking of objects whose shape can be approximated to a polygon mesh it’s presented. The proposed method takes advantage of the fact that polygon meshes may be composed of quadrilaterals, which can be tracked in 2D ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 78, Issue 9

May 2019

1514 pages

ISSN:1380-7501

Issue’s Table of Contents

Copyright © Copyright © 2019 Springer Science+Business Media, LLC, part of Springer Nature.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 May 2019

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 09 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Li WWang JWei M(2024)Local geometric edge features based registration for textureless object in augmented reality assisted assemblyVirtual Reality10.1007/s10055-023-00922-928:1Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1007/s10055-023-00922-9
Li JZhong FXu SQin X(2021)3D Object Tracking with Adaptively Weighted Local BundlesJournal of Computer Science and Technology10.1007/s11390-021-1272-536:3(555-571)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s11390-021-1272-5
Zhou DChai ZLiu CLi PXiong Z(2020)6D Pose Estimation for Texture-Less Industrial Parts in the CrowdIntelligent Robotics and Applications10.1007/978-3-030-66645-3_35(415-428)Online publication date: 5-Nov-2020
https://dl.acm.org/doi/10.1007/978-3-030-66645-3_35

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents