Browse > Article
http://dx.doi.org/10.3837/tiis.2018.11.018

Visual Tracking Using Improved Multiple Instance Learning with Co-training Framework for Moving Robot  

Zhou, Zhiyu (School of Information Science and Technology, Zhejiang Sci-Tech University)
Wang, Junjie (School of Information Science and Technology, Zhejiang Sci-Tech University)
Wang, Yaming (School of Information Science and Technology, Zhejiang Sci-Tech University)
Zhu, Zefei (School of Mechanical Engineering, Hangzhou Dianzi University)
Du, Jiayou (School of Mechanical Engineering, Hangzhou Dianzi University)
Liu, Xiangqi (School of Mechanical Engineering, Hangzhou Dianzi University)
Quan, Jiaxin (School of Information Science and Technology, Zhejiang Sci-Tech University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.12, no.11, 2018 , pp. 5496-5521 More about this Journal
Abstract
Object detection and tracking is the basic capability of mobile robots to achieve natural human-robot interaction. In this paper, an object tracking system of mobile robot is designed and validated using improved multiple instance learning algorithm. The improved multiple instance learning algorithm which prevents model drift significantly. Secondly, in order to improve the capability of classifiers, an active sample selection strategy is proposed by optimizing a bag Fisher information function instead of the bag likelihood function, which dynamically chooses most discriminative samples for classifier training. Furthermore, we integrate the co-training criterion into algorithm to update the appearance model accurately and avoid error accumulation. Finally, we evaluate our system on challenging sequences and an indoor environment in a laboratory. And the experiment results demonstrate that the proposed methods can stably and robustly track moving object.
Keywords
object tracking; multiple instance learning; active learning; co-training; moving robot;
Citations & Related Records
연도 인용수 순위
  • Reference
1 K. Zhang, L .Zhang, M. H. Yang, "Real-time compressive tracking," in Proc. of European Conference on Computer Vision, pp.864-877, 2012.
2 S. Avidan, "Support vector tracking," IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 26, no. 8, pp. 1064, 2004.   DOI
3 S. Avidan, "Ensemble tracking," IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 29, no. 2, pp. 261-271, 2007.   DOI
4 R. T. Collins, Y. Liu, M. Leordeanu, "Online selection of discriminative tracking features," IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 27, no. 10, pp.1631-1643, 2005.   DOI
5 H. Grabner, M. Grabner, H. Bischof, "Real-time tracking via on-line boosting," in Proc. of British Machine Vision Conference 2006, pp.47-56, 2006.
6 B. Babenko, M. H. Yang, S. Belongie, "Robust object tracking with online multiple instance learning," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 8, pp.1619-1632, 2011.   DOI
7 K. Zhang, H. Song, "Real-time visual tracking via online weighted multiple instance learning," Pattern Recognition, vol. 46, no.1, pp.397-411, 2013.   DOI
8 H. Grabner, C. Leistner, H. Bischof, "Semi-supervised on-line boosting for robust tracking," in Proc. of European conference on computer vision, pp. 234-247, 2008.
9 K. Zhang, L. Zhang, Q. Liu, et al., "Fast visual tracking via dense spatio-temporal context learning," in Proc. of European Conference on Computer Vision, pp.127-141, 2014.
10 D. Wang, H. Lu, M. H. Yang, "Least soft-threshold squares tracking," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp.2371-2378, 2013.
11 Y.-D.Zhang, Y. Zhang, X.-X.Hou, et al., "Seven-layer deep neural network based on sparse autoencoder for voxelwise detection of cerebral microbleed," Multimedia Tools and Applications, vol. 77, no. 9, pp. 10521-10538, 2018.   DOI
12 S.-H. Wang, Y.D. Lv, Y. Sui, et al., "Alcoholism detection by data augmentation and convolutional neural network with stochastic pooling," Journal of Medical Systems, vol. 42, no. 1, Article ID: 2, 2018.
13 B. Settles, "Active learning literature survey," University of Wisconsin, Madison, 2010.
14 T. M. Cover, J.A. Thomas, "Elements of information theory," John Wiley & Sons, 2005.
15 D. Zhang, F. Wang, Z. Shi, et al., "Interactive localized content based image retrieval with multiple-instance active learning," Pattern Recognition, vol. 43, no. 2, pp. 478-484, 2010.   DOI
16 F. Tang, S. Brennan, Q. Zhao, et al., "Co-tracking using semi-supervised support vector machines," in Proc. of IEEE International Conference on Computer Vision, pp.1-8, 2007.
17 Q. Yu, T. B. Dinh, G. Medioni, "Online tracking and reacquisition using co-trained generative and discriminative trackers," in Proc. of European conference on computer vision, pp. 678-691, 2008.
18 R. Liu, J. Cheng, H. Lu, "A robust boosting tracker with minimum error bound in a co-training framework," in Proc. of IEEE International Conference on Computer Vision, pp.1459-1466, 2009.
19 S. Liu, M. Lu, G.Liu, et al., "A novel distance metric: generalized relative entropy," Entropy, vol. 19, no. 6, Article ID: 269, 2017.
20 Y. Wu, J. Lim, M. H. Yang, "Object tracking benchmark," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 9, pp. 1834-1848, 2015.   DOI
21 W. Choi, C. Pantofaru, S. Savarese, "A general framework for tracking multiple people from a moving camera," IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. 7, pp.1577-1591, 2013.   DOI
22 Z. Pan, S. Liu, W. Fu, "A review of visual moving target tracking," Multimedia Tools and Applications, vol. 76, no. 16, pp. 16989-17018, 2017.   DOI
23 S. Liu, Z. Pan, X. Cheng, "A novel fast fractal image compression method based on distance clustering in high dimensional sphere surface," Fractals-Complex Geometry Patterns and Scaling in Nature and Society, vol. 25, no. 4 , Article ID: 1740004, 2017.
24 Y.-D. Zhang, Y. Zhang, Y.-D. Lv, et al., "Alcoholism detection by medical robots based on Hu moment invariants and predator-prey adaptive-inertia chaotic particle swarm optimization," Computers & Electrical Engineering, vol. 63, pp. 126-138, 2017.   DOI
25 S. Liu, Z. Pan, H. Song, "Digital image watermarking method based on DCT and fractal encoding," IET Image Processing, vol. 11, no. 10, pp. 815-821, 2017.   DOI
26 W. Kim, J. Chun, "An improved approach for 3D hand pose estimation based on a single depth image and Haar random forest," KSII Transactions on Internet and Information Systems, vol. 9, no.8, pp. 3136-3150, 2015.   DOI
27 R. Liu, Z. Du, L. Sun, "Moving object tracking based on mobile robot vision," in Proc. of International Conference on Mechatronics and Automation, pp.3625-3630, 2009.
28 S. Kim, J. Park, J. M. Lee, "Implementation of tracking and capturing a moving object using a mobile robot," International Journal of Control Automation & Systems, vol. 3, no. 3, pp. 444-452, 2005.
29 Z. Kalal, K. Mikolajczyk, J. Matas, "Tracking-learning-detection," IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. 7, Article ID: 1409, 2012.
30 S. Hare, S. Golodetz, A. Saffari, et al., "Struck: structured output tracking with kernels," IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 38, no.10, pp. 2096-2109, 2016.   DOI
31 H. Lang, Y. Wang, W. D. S. Clarence, "Vision based object identification and tracking for mobile robot visual servo control," in Proc. of IEEE International Conference on Control and Automation, pp.92-96, 2010.
32 D. A. Ross, J. Lim, R. S. Lin, et al. "Incremental learning for robust visual tracking," International Journal of Computer Vision, vol. 77, no. 1, pp. 125-141, 2008.   DOI
33 X. Mei, H. Ling, "Robust visual tracking and vehicle classification via sparse representation," IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no.11, pp. 2259-72, 2011.   DOI