Journal of Computer Science and Technology ›› 2021, Vol. 36 ›› Issue (3): 555-571.doi: 10.1007/s11390-021-1272-5

Special Issue: Computer Graphics and Multimedia

• Special Section of CVM 2021 • Previous Articles     Next Articles

3D Object Tracking with Adaptively Weighted Local Bundles

Jiachen Li1, Fan Zhong2,*, Member, CCF, Songhua Xu3, and Xueying Qin1,*, Senior Member, CCF, Member, IEEE        

  1. 1 School of Software, Shandong University, Jinan 250101, China;
    2 School of Computer Science and Technology, Shandong University, Qingdao 266237, China;
    3 College of Engineering and Computing, University of South Carolina, Columbia 29208, U.S.A.
  • Received:2021-01-06 Revised:2021-04-23 Online:2021-05-05 Published:2021-05-31
  • Contact: Fan Zhong, Xueying Qin E-mail:zhongfan@sdu.edu.cn;qxy@sdu.edu.cn
  • About author:Jiachen Li is a Ph.D. candidate of School of Software, Shandong University, Jinan. He received his B.E. degree in exploration technology and engineering from Ocean University of China, Qingdao, in 2016. His research interests include augmented reality, 3D object tracking, pose estimation, head posture analysis, etc.
  • Supported by:
    This work was partially supported by Zhejiang Lab under Grant No. 2020NB0AB02, and the Industrial Internet Innovation and Development Project in 2019 of China.

The 3D object tracking from a monocular RGB image is a challenging task. Although popular color and edgebased methods have been well studied, they are only applicable to certain cases and new solutions to the challenges in real environment must be developed. In this paper, we propose a robust 3D object tracking method with adaptively weighted local bundles called AWLB tracker to handle more complicated cases. Each bundle represents a local region containing a set of local features. To alleviate the negative effect of the features in low-confidence regions, the bundles are adaptively weighted using a spatially-variant weighting function based on the confidence values of the involved energy terms. Therefore, in each frame, the weights of the energy items in each bundle are adapted to different situations and different regions of the same frame. Experiments show that the proposed method can improve the overall accuracy in challenging cases. We then verify the effectiveness of the proposed confidence-based adaptive weighting method using ablation studies and show that the proposed method overperforms the existing single-feature methods and multi-feature methods without adaptive weighting.

Key words: 3D tracking; local bundle; feature fusion; confidence map;

[1] Lepetit V, Fua P. Monocular model-based 3D tracking of rigid objects:A survey. Found. Trendsr in Comput. Graph. Vis., 2005, 1(1):1-89. DOI:10.1561/0600000001
[2] Vacchetti L, Lepetit V, Fua P. Stable real-time 3D tracking using online and offline information. IEEE Trans. Pattern Anal. Mach. Intell., 2004, 26(10):1385-1391. DOI:10.1109/TPAMI.2004.92.
[3] Lourakis M I A, Zabulis X. Model-based pose estimation for rigid objects. In Proc. the 9th International Conference on Computer Vision Systems, July 2013, pp.83-92. DOI:10.1007/978-3-642-39402-7_9.
[4] Tan D J, Tombari F, Ilic S, Navab N. A versatile learning-based 3D temporal tracker:Scalable, robust, online. In Proc. the 2015 IEEE International Conference on Computer Vision, December 2015, pp.693-701. DOI:10.1109/ICCV.2015.86.
[5] Besl P J, McKay N D. A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell., 1992, 14(2):239-256. DOI:10.1109/34.121791.
[6] Peng S, Liu Y, Huang Q, Zhou X, Bao H. PVNet:Pixel-wise voting network for 6DoF pose estimation. In Proc. the 2019 IEEE Conference on Computer Vision and Pattern Recognition, June 2019, pp.4561-4570. DOI:10.1109/CVPR.2019.00469.
[7] Ye Y, Zhang C, Hao X. ARPNET:Attention region proposal network for 3D object detection. Sci. China Inf. Sci., 2019, 62(12):Article No. 220104. DOI:10.1007/s11432-019-2636-x.
[8] Garon M, Lalonde J. Deep 6-DOF tracking. IEEE Trans. Vis. Comput. Graph., 2017, 23(11):2410-2418. DOI:10.1109/TVCG.2017.2734599.
[9] Li Y, Wang G, Ji X, Xiang Y, Fox D. DeepIM:Deep iterative matching for 6D pose estimation. Int. J. Comput. Vis., 2020, 128(3):657-678. DOI:10.1007/s11263-019-01250-9.
[10] Harris C, Stennett C. RAPID-A video rate object tracker. In Proc. the 1990 British Machine Vision Conference, September 1990, pp.73-77. DOI:10.5244/C.4.15.
[11] Seo B, Park H, Park J, Hinterstoisser S, Ilic S. Optimal local searching for fast and robust textureless 3D object tracking in highly cluttered backgrounds. IEEE Trans. Vis. Comput. Graph., 2014, 20(1):99-110. DOI:10.1109/TVCG.2013.94.
[12] Wang G, Wang B, Zhong F, Qin X, Chen B. Global optimal searching for textureless 3D object tracking. The Visual Computer, 2015, 31(6/7/8):979-988. DOI:10.1007/s00371-015-1098-7.
[13] Wang B, Zhong F, Qin X. Robust edge-based 3D object tracking with direction-based pose validation. Multimedia Tools Appl., 2019, 78(9):12307-12331. DOI:10.1007/s11042-018-6727-5.
[14] Zhang Y, Li X, Liu H, Shang Y. Comparative study of visual tracking method:A probabilistic approach for pose estimation using lines. IEEE Trans. Circuits Syst. Video Technol., 2017, 27(6):1222-1234. DOI:10.1109/TCSVT.2016.2527219.
[15] Prisacariu V A, Reid I D. PWP3D:Real-time segmentation and tracking of 3D objects. Int. J. Comput. Vis., 2012, 98(3):335-354. DOI:10.1007/s11263-011-0514-3.
[16] Tjaden H, Schwanecke U, Schömer E. Real-time monocular segmentation and pose tracking of multiple objects. In Proc. the 14th European Conference on Computer Vision, October 2016, pp.423-438. DOI:10.1007/978-3-319-46493-0_26.
[17] Hexner J, Hagege R R. 2D-3D pose estimation of heterogeneous objects using a region based approach. Int. J. Comput. Vis., 2016, 118(1):95-112. DOI:10.1007/s11263-015-0873-2.
[18] Tjaden H, Schwanecke U, Schömer E. Real-time monocular pose estimation of 3D objects using temporally consistent local color histograms. In Proc. the 2017 IEEE International Conference on Computer Vision, October 2017, pp.124-132. DOI:10.1109/ICCV.2017.23.
[19] Tjaden H, Schwanecke U, Schömer E, Cremers D. A region-based gauss-newton approach to real-time monocular multiple object tracking. IEEE Trans. Pattern Anal. Mach. Intell., 2019, 41(8):1797-1812. DOI:10.1109/TPAMI.2018.2884990.
[20] Marchand É, Bouthemy P, Chaumette F. A 2D-3D model-based approach to real-time visual tracking. Image Vis. Comput., 2001, 19(13):941-955. DOI:10.1016/S0262-8856(01)00054-3.
[21] Drummond T, Cipolla R. Real-time visual tracking of complex structures. IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24(7):932-946. DOI:10.1109/TPAMI.2002.1017620.
[22] Wuest H, Vial F, Stricker D. Adaptive line tracking with multiple hypotheses for augmented reality. In Proc. the 4th IEEE/ACM International Symposium on Mixed and Augmented Reality, October 2005, pp.62-69. DOI:10.1109/ISMAR.2005.8.
[23] Choi C, Christensen H I. Robust 3D visual tracking using particle filtering on the special Euclidean group:A combined approach of keypoint and edge features. The International Journal of Robotics Research, 2012, 31(4):498-519. DOI:10.1177/0278364912437213.
[24] Wang B, Zhong F, Qin X. Pose optimization in edge distance field for textureless 3D object tracking. In Proc. the 2017 Computer Graphics International Conference, June 2017, Article No. 32. DOI:10.1145/3095140.3095172.
[25] Osher S, Sethian J A. Fronts propagating with curvaturedependent speed:Algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics, 1988, 79(1):12-49. DOI:10.1016/0021-9991(88)90002-2.
[26] Zhong L, Zhao X, Zhang Y, Zhang S, Zhang L. Occlusionaware region-based 3D pose tracking of objects with temporally consistent polar-based local partitioning. IEEE Trans. Image Process., 2020, 29:5065-5078. DOI:10.1109/TIP.2020.2973512.
[27] Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11):2278-2324. DOI:10.1109/5.726791.
[28] Crivellaro A, Rad M, Verdie Y, Yi K M, Fua P, Lepetit V. Robust 3D object tracking from monocular images using stable parts. IEEE Trans. Pattern Anal. Mach. Intell., 2018, 40(6):1465-1479. DOI:10.1109/TPAMI.2017.2708711.
[29] Zhong L, Zhang L. A robust monocular 3D object tracking method combining statistical and photometric constraints. Int. J. Comput. Vis., 2019, 127(8):973-992. DOI:10.1007/s11263-018-1119-x.
[30] Ma Y, Soatto S, Košecká J, Sastry S S. An Invitation to 3-D Vision:From Images to Geometric Models (1st edition). Springer-Verlag New York Publishers, 2004.
[31] Zhong F, Qin X, Chen J, Hua W, Peng Q. Confidencebased color modeling for online video segmentation. In Proc. the 9th Asian Conference on Computer Vision, September 2009, pp.697-706. DOI:10.1007/978-3-642-12304-766.
[32] Wu P, Lee Y, Tseng H, Ho H, Yang M, Chien S. A benchmark dataset for 6DoF object pose tracking. In Proc. the 2017 IEEE International Symposium on Mixed and Augmented Reality Adjunct, October 2017, pp.186-191. DOI:10.1109/ISMAR-Adjunct.2017.62.
[33] Brachmann E, Michel F, Krull A, Yang M Y, Gumhold S, Rother C. Uncertainty-driven 6D pose estimation of objects and scenes from a single RGB image. In Proc. the 2016 IEEE Conference on Computer Vision and Pattern Recognition, June 2016, pp.3364-3372. DOI:10.1109/CVPR.2016.366.
[34] Whelan T, Leutenegger S, Salas-Moreno R F, Glocker B, Davison A J. ElasticFusion:Dense SLAM without a pose graph. In Proc. the 2015 Robotics:Science and Systems, July 2015. DOI:10.15607/RSS.2015.XI.001.
[35] Mur-Artal R, Tardós J D. ORB-SLAM2:An open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robotics, 2017, 33(5):1255-1262. DOI:10.1109/TRO.2017.2705103.
[36] Marchand É, Uchiyama H, Spindler F. Pose estimation for augmented reality:A hands-on survey. IEEE Trans. Vis. Comput. Graph., 2016, 22(12):2633-2651. DOI:10.1109/TVCG.2015.2513408.
[37] Cheng M, Liu Y, Lin W, Zhang Z, Rosin P L, Torr P H S. BING:Binarized normed gradients for objectness estimation at 300fps. Comput. Vis. Media, 2019, 5(1):3-20. DOI:10.1007/s41095-018-0120-1.
[1] Cheng-Zhang Zhu, Rong Hu, Bei-Ji Zou, Rong-Chang Zhao, Chang-Long Chen, Ya-Long Xiao. Automatic Diabetic Retinopathy Screening via Cascaded Framework Based on Image- and Lesion-Level Features Fusion [J]. Journal of Computer Science and Technology, 2019, 34(6): 1307-1318.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] Chen Shihua;. On the Structure of Finite Automata of Which M Is an(Weak)Inverse with Delay τ[J]. , 1986, 1(2): 54 -59 .
[2] Wang Jianchao; Wei Daozheng;. An Effective Test Generation Algorithm for Combinational Circuits[J]. , 1986, 1(4): 1 -16 .
[3] Min Yinghua;. Easy Test Generation PLAs[J]. , 1987, 2(1): 72 -80 .
[4] Sun Yongqiang; Lu Ruzhan; Huang Xiaorong;. Termination Preserving Problem in the Transformation of Applicative Programs[J]. , 1987, 2(3): 191 -201 .
[5] Qi Yulu;. A Systolic Approach for an Improvement of a Finite Field Multiplier[J]. , 1987, 2(4): 303 -309 .
[6] Feng Yulin;. Hierarchical Protocol Analysis by Temporal Logic[J]. , 1988, 3(1): 56 -69 .
[7] Min Yinghua; Yashwant K. Malaiya; Jin Boping;. Aliasing Errors in Parallel Signature Analyzers[J]. , 1990, 5(1): 24 -40 .
[8] Zhu Mingyuan;. Two Congruent Semantics for Prolog with CUT[J]. , 1990, 5(1): 82 -91 .
[9] Xu Jie; Li Qingnan; Huang Shize; Xu Jiangfeng;. DFTSNA:A Distributed Fault-Tolerant Shipboard System[J]. , 1990, 5(2): 109 -116 .
[10] Li Jintao; Min Yinghua;. Product-Oriented Test-Pattern Generation for Programmable Logic Arrays[J]. , 1990, 5(2): 164 -174 .

ISSN 1000-9000(Print)

         1860-4749(Online)
CN 11-2296/TP

Home
Editorial Board
Author Guidelines
Subscription
Journal of Computer Science and Technology
Institute of Computing Technology, Chinese Academy of Sciences
P.O. Box 2704, Beijing 100190 P.R. China
Tel.:86-10-62610746
E-mail: jcst@ict.ac.cn
 
  Copyright ©2015 JCST, All Rights Reserved