Design of tracker for mobile smart devices

Jing-ying XIONG; Ming DAI

doi:10.3788/OPE.20172512.3152

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Design of tracker for mobile smart devices

Information Sciences | 更新时间：2020-07-07

- Design of tracker for mobile smart devices
- Optics and Precision Engineering Vol. 25, Issue 12, Pages: 3152-3159(2017)
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所, 吉林长春, 130000
  2.中国科学院大学大珩学院, 吉林长春 130000
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20172512.3152
  CLC： TP273;TP391.4
- Received：23 February 2017，
  
  Accepted：08 April 2017，
  
  Published：25 December 2017
- 稿件说明：
移动端阅览
Jing-ying XIONG, Ming DAI. Design of tracker for mobile smart devices[J]. Optics and precision engineering, 2017, 25(12): 3152-3159.
DOI：

Jing-ying XIONG, Ming DAI. Design of tracker for mobile smart devices[J]. Optics and precision engineering, 2017, 25(12): 3152-3159. DOI： 10.3788/OPE.20172512.3152.

摘要

针对增强现实技术在移动智能设备上应用需求，设计了一种适应移动智能设备嵌入式系统的目标跟踪器。在特征描绘阶段采用亮度信息进行二值特征的快速分割并建立强显著性的二值特征描绘器；在特征选择阶段提出一种稀疏跟踪搜索模板进一步提高跟踪算法的执行效率。然后，在跟踪器中建立了存储目标初始信息的静态库与不断更新目标外观或运动变化信息的动态库，通过对比静、动态库与搜索模板区域中的信息确立跟踪目标位置。对跟踪器执行时间进行了对比，结果表明：在保证较高的跟踪精度条件下，采用稀疏搜索模板能明显改善算法的执行时间，将搜索半径设为10可在绝大多数情况下满足跟踪器的实时要求。对跟踪器的有效能力亦进行了对比，结果显示：在3组不同搜索半径下，提出的DBRISK描绘方法的平均跟踪误差相对于BRISK（Binary Robust Invariant Scalable Keypoints）方法分别下降了16%、28%和29%。实验表明：提出的方法能够明显改善跟踪器的信息提取准确度，适用于计算能力有限的移动智能设备。

Abstract

To apply augmented reality technology in mobile smart devices

a novel target tracker for the embedded system of a smart mobile device was proposed. In the stage of feature description

the binary feature was segmented rapidly and discriminative binary descriptors were established by fast brightness segment. In the stage of feature matching

a sparse searching template was proposed to improve the execution efficiency of the tracking algorithm. Initial target information was stored in a static library established by the tracker and the change of target appearance was stored in a dynamic library

and the target location was tracked by comparing search information and the templates in libraries. The execution time of the tracker was compared

and the results show that sparse search templates significantly improve the execution time of the algorithm at maintaining a higher tracking accuracy

and setting the search radius to 10 is able to meet the real-time requirements of trackers. The effective capacity of the tracker was compared also. And the results indicate that the tracking error of DBRISK are 16%

28% and 29% decrease than those of the original BRISK (Binary Robust Invariant Scalable Keypoints) under three different search radii

which means the proposed method significantly improves the accuracy of tracking method

and is applicable to the limited computing power smart mobile devices.

关键词

Keywords

references

陈和恩. 基于单目摄像机的增强现实场景感知技术研究[D]. 广州: 广东工业大学, 2016. http://cdmd.cnki.com.cn/Article/CDMD-11845-1016156522.htm

CHEN H N. Monocular Camera based Perception in Augmented Reality Scene [D]. Guangzhou:Guangdong University of Technology, 2016. (in Chinese)

聂海涛. 基于图像局部特征的康复机器人目标识别方法研究[D]. 长春: 中国科学院长春光学精密机械与物理研究所, 2015. http://cdmd.cnki.com.cn/Article/CDMD-80139-1015330247.htm

NIE H T. Study of Rehabilitative Robtics Object Recognition Method Based on Local Image Feature [D]. Changchun:Institute of Optics, Fine Mechanic and Physics, Chinese Academy of Sciences, 2015. (in Chinese)

刘迎, 王朝阳, 高楠, 等.特征提取的点云自适应精简[J].光学精密工程, 2017, 25(1):245-254.

LIU Y, WANG CH Y, GAO N, et al.. Point cloud adaptive simplification of feature extraction[J]. Opt. Precision Eng., 2017, 25(1):245-254. (in Chinese)

屈玉福, 刘子悦, 江云秋, 等.自适应变尺度特征点提取方法[J].光学精密工程, 2017, 25(1):188-197.

QU Y F, LIU Z Y, JIANG Y Q, et al.. Self-adaptative variable-metric feature point extraction method[J]. Opt. Precision Eng., 2017, 25(1):188-197. (in Chinese)

WAGNER D, REITMAYR G, MULLONI A, et al.. Real-time detection and tracking for augmented reality on mobile phones[J]. IEEE Transactions on Visualization and Computer Graphics, 2010, 16(3):355-368.

MAIR E, HAGER G D, BURSCHKA D, et al .. Adaptive and generic corner detection based on the accelerated segment test[C]. Proceedings of the European Conference on Computer Vision ( ECCV '10), Springer , 2010. http://www.springerlink.com/content/5nv7941h32t65740

LOWE D G. Object recognition from local scale-invariant features[C]. The proceedings of the 17 th IEEE International Conference on Computer Vision , IEEE , 1999, 1150-1157. https://www.mendeley.com/research-papers/object-recognition-local-scaleinvariant-features-sift/

何林阳, 刘晶红, 李刚, 等.改进BRISK特征的快速图像配准算法[J].红外与激光工程, 2014, 43(8):2722-2727.

HE L Y, LIU J H, LI G, et al.. Fast image registration approach based on improved BRISK[J]. Infrared and Laser Engineering, 2014, 43(8):2722-2727. (in Chinese)

吉淑娇, 朱明, 雷艳敏, 等.基于改进运动矢量估计法的视频稳像[J].光学精密工程, 2015, 23(5):1458-1465.

JI SH J, ZHU M, LEI Y M, et al.. Video stabilization with improved motion vector estimation[J]. Opt. Precision Eng., 2015, 23(5):1458-1465. (in Chinese)

WHITEN C, LAGANI? RE R, BILODEAU G A. Efficient action recognition with MoFREAK[C]//2013 International Conference on Computer and Robot Vision ( CRV ), IEEE , 2013.

MINNEHAN B, SPANG H, SAVAKIS A E. Robust and Efficient Tracker Using Dictionary of Binary Descriptors and Locality Constraints [M]//Bebis G. Advances in Visual Computing. ISVC 2014. Cham:Springer, 2014.

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