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Frontiers of Optoelectronics

Front. Optoelectron.    2019, Vol. 12 Issue (4) : 413-421     https://doi.org/10.1007/s12200-019-0862-0
RESEARCH ARTICLE
Robot visual guide with Fourier-Mellin based visual tracking
Chao PENG, Danhua CAO(), Yubin WU, Qun YANG
School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
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Abstract

Robot vision guide is an important research area in industrial automation, and image-based target pose estimation is one of the most challenging problems. We focus on target pose estimation and present a solution based on the binocular stereo vision in this paper. To improve the robustness and speed of pose estimation, we propose a novel visual tracking algorithm based on Fourier-Mellin transform to extract the target region. We evaluate the proposed tracking algorithm on online tracking benchmark-50 (OTB-50) and the results show that it outperforms other lightweight trackers, especially when the target is rotated or scaled. The final experiment proves that the improved pose estimation approach can achieve a position accuracy of 1.84 mm and a speed of 7 FPS (frames per second). Besides, this approach is robust to the variances of illumination and can work well in the range of 250-700 lux.

Keywords robot visual guide      target pose estimation      stereo vision      visual tracking      Fourier-Mellin transform (FMT)     
Corresponding Authors: Danhua CAO   
Online First Date: 11 June 2019    Issue Date: 30 December 2019
 Cite this article:   
Chao PENG,Danhua CAO,Yubin WU, et al. Robot visual guide with Fourier-Mellin based visual tracking[J]. Front. Optoelectron., 2019, 12(4): 413-421.
 URL:  
http://journal.hep.com.cn/foe/EN/10.1007/s12200-019-0862-0
http://journal.hep.com.cn/foe/EN/Y2019/V12/I4/413
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Chao PENG
Danhua CAO
Yubin WU
Qun YANG
Fig.1  Robot visual guide algorithm workflow
Fig.2  Flowchart of our tracker, red line (III) represents training stage of last frame, blue line (I, II) represents detect stage of current frame
Fig.3  Robot visual guide experiment system
sequence ours KCF CXT VTD Struck SCM ASLA CSK L1APG attributes
Walking 0.67 0.53 0.17 0.61 0.57 0.71 0.77 0.54 0.75 SV
Walking2 0.62 0.40 0.37 0.33 0.51 0.80 0.37 0.46 0.76 SV
Car2 0.79 0.68 0.87 0.80 0.69 0.92 0.87 0.69 0.91 SV
Car24 0.73 0.43 0.79 0.35 0.14 0.88 0.80 0.38 0.78 SV
Car4 0.76 0.48 0.31 0.36 0.49 0.76 0.75 0.47 0.25 SV
Jogging1 0.66 0.19 0.77 0.15 0.17 0.18 0.18 0.18 0.17 OPR
Singer1 0.67 0.35 0.49 0.49 0.36 0.86 0.79 0.36 0.28 SV OPR
Human6 0.59 0.21 0.15 0.18 0.21 0.35 0.38 0.21 0.25 SV OPR
Tiger2 0.63 0.35 0.36 0.30 0.54 0.09 0.15 0.17 0.24 IPR OPR
Doll 0.74 0.53 0.75 0.65 0.55 0.83 0.83 0.32 0.45 SV IPR OPR
FPS 50 275 15.3 5.7 20.2 0.51 8.5 362 2
Tab.1  Comparison of average overlap score
Fig.4  Success plots on OTB-50, the AUC scores are shown in the legend
Fig.5  Snapshot of our tracker. (a) Tiger2; (b) Walking2
Fig.6  Success rate plot
2.0 mm 2.3 mm 2.5 mm average position error
82.7% 99.3% 100% 1.84 mm
Tab.2  Average distance and success rate in three typical threshold
algorithm time expense/ms
before apply tracking after apply tracking
stereo rectify 7.46 7.46
ROI extract 61.21 32.32
stereo matching 14.18 14.18
pose estimation 83.88 83.88
total 166.73 137.84
Tab.3  Time expense
number 1 2 3 4 5 6 7
illumination/lux 98 125 143 186 217 238 248
5 mm success rate 10% 57% 61% 50% 62% 88% 100%
number 8 9 10 11 12 13 14
illumination/lux 276 298 340 432 534 703 823
5 mm success rate 100% 100% 100% 100% 98% 98% 54%
Tab.4  Detail information of the illumination adaptability experiments
Fig.7  Illumination adaptability experiments
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