OBJECT TRACKING BASED ON MACHINE VISION AND IMPROVED SVDD ALGORITHM

Publications

Share / Export Citation / Email / Print / Text size:

International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering , Engineering, Electrical & Electronic

GET ALERTS

eISSN: 1178-5608

DESCRIPTION

6
Reader(s)
13
Visit(s)
0
Comment(s)
0
Share(s)

VOLUME 8 , ISSUE 1 (March 2015) > List of articles

OBJECT TRACKING BASED ON MACHINE VISION AND IMPROVED SVDD ALGORITHM

Yongqing Wang * / Yanzhou Zhang

Keywords : Object tracking, machine vision, Support Vector Data Description, one-class SVM, classification.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 8, Issue 1, Pages 677-696, DOI: https://doi.org/10.21307/ijssis-2017-778

License : (CC BY-NC-ND 4.0)

Received Date : 02-November-2014 / Accepted: 31-January-2015 / Published Online: 01-March-2015

ARTICLE

ABSTRACT

Object tracking is an important research topic in the applications of machine vision, and has made great progress in the past decades, among which the technique based on classification is a very efficient way to solve the tracking problem. The classifier classifies the objects and background into two different classes, where the tracking drift caused by noisy background can be effectively handled by one-class SVM. But the time and space complexities of traditional one-class SVM methods tend to be high, which makes it do not scale well with the number of training sample, and limits its wide applications. Based on the idea proposed by Support Vector Data Description, we present an improved
SVDD algorithm to handle object tracking efficiently. The experimental results on synthetic data, tracking results on car and plane demonstrate the validity of the proposed algorithm.

Content not available PDF Share

FIGURES & TABLES

REFERENCES

[1] Wei He, “Study on the method of target feature selection in video tracking”, Tsinghua,
Beijing, 2007.
[2] I. Sun and M. Zhang, “Based on Kalman Filter the Mobile Manipulator Non-Calibration Free
Dynamic Target Tracking Technology Research”, Proceedings of 2009 International Conference
on Electronic Computer Technology, 2009, pp. 610-614.
[3] Alper Yilmaz , Omar Javed and Mubarak Shah, “Object tracking: A survey”, ACM
Computing Surveys, Vol. 38, No. 4, 2006, 1-45.
[4] C. VEENMAN, M. REINDERS and E. BACKER, “Resolving motion correspondence for densely moving points”, IEEE Trans. Patt. Analy. Mach. Intell, Vol. 23, No. 1, 2001, pp. 54-72. [5] I. SETHI and R. JAIN, “Finding trajectories of feature points in a monocular image sequence”, IEEE Trans. Patt. Analy. Mach. Intell, Vol. 9, No. 1, 1987, pp. 56-73. [6] S. INTILLE, J. DAVIS, and A. BOBICK, “Real-time closed-world tracking”, In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1997, pp. 697-703. [7] K. SHAFIQUE and M. SHAH, “A non-iterative greedy algorithm for multi-frame point correspondence”, In IEEE Intern. Conference on Comp. Vision (ICCV), 2003, pp. 110-115. [8] M. ISARD and A. BLAKE, “Condensation-conditional density propagation for visual tracking”, Int. J. Comput. Vision, Vol. 29, No. 1, 1998, pp. 5-28. [9] S. ZHOU, R. CHELLAPA and B. MOGHADAM, “Adaptive visual tracking and recognition using particle filters”, In Proceedings IEEE International Conference on Multimedia and Expo. (ICME), 2003, pp. 349-352. [10] N. VASWANI, A. ROYCHOWDHURY and R. CHELLAPPA, “Activity recognition using the dynamics of the configuration of interacting objects”, In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2003, pp. 633-640. [11] S. BIRCHFIELD, “Elliptical head tracking using intensity gradients and color histograms”, In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1998, pp. 232-237. [12] P. FIEGUTH and D. TERZOPOULOS, “Color-based tracking of heads and other mobile objects at video frame rates”, In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1997, pp. 21-27. [13] H. SCHWEITZER, J. W. BELL and F. WU, “Very fast template matching”, In European Conference on Computer Vision (ECCV), 2002, pp. 358-372. [14] D. COMANICIU, V. RAMESH and P. MEER, “Kernel-based object tracking”, IEEE Trans. Patt. Analy. Mach. Intell, Vol. 25, 2003, pp. 564-575. [15] A. JEPSON, D. FLEET and T. ELMARAGHI, “Robust online appearance models for visual tracking”, IEEE Trans. Patt. Analy. Mach. Intell, Vol. 25, No. 10, 2003, pp. 1296-1311. [16] M. BLACK and A. JEPSON, “Eigen-tracking: Robust matching and tracking of articulated objects using a view-based representation”, Int. J. Comput. Vis., Vol. 26, No. 1, 1998, pp. 63-84. [17] S. AVIDAN, “Support vector tracking”, In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2001, pp. 184-191. [18] D. HUTTENLOCHER, J. NOH and W. RUCKLIDGE, “Tracking non-rigid objects in complex scenes”, In IEEE Intern. Conference on Computer Vision (ICCV), 1993, pp. 93-101. [19] J. KANG, I. COHEN and G. MEDIONI, “Continuous tracking within and across camera streams”, In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2003, pp. 267-272. [20] J. KANG, I. COHEN and G. MEDIONI, “Object reacquisition using geometric invariant appearance model”, In Intern. Conference on Pattern Recognition (ICPR), 2004, pp. 759-762. [21] J. MACCORMICK and A. BLAKE, “Probabilistic exclusion and partitioned sampling for multiple object tracking”, Int. J. Comput. Vision, Vol. 39, No. 1, 2000, pp. 57-71. [22] Y. CHEN, Y. RUI, and T. HUANG, “JPDAF based HMM for real-time contour tracking”, In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2001, pp. 543-550. [23] A. MANSOURI, “Region tracking via level set PDES without motion computation”, IEEE Trans. Patt. Analy. Mach. Intell, Vol. 24, No, 7, 2002, pp. 947-961. [24] D. CREMERS and C. SCHNORR, “Statistical shape knowledge in variational motion segmentation”, Image and Vision Computing, Vol. 21, 2003, pp. 77-86. [25] A.YILMAZ, X. LI and M. SHAH, “Contour based object tracking with occlusion handling in video acquired using mobile cameras”, IEEE Trans. Patt. Analy. Mach. Intell., Vol. 26, No. 11, 2004, pp. 1531-1536. [26] N.K. Suryadevara, S.C. Mukhopadhyay, R. Wang, R.K. Rayudu, Forecasting the behavior of an elderly using wireless sensors data in a smart home, Engineering Applications of Artificial Intelligence, Volume 26, Issue 10, November 2013, Pages 2641-2652, ISSN 0952-1976, http://dx.doi.org/10.1016/j.engappai.2013.08.004.
[27] D. M. J. Tax and R. P. W. Duin, “Support Vector Domain Description”, Pattern Recognition Letters, Vol. 20, No. 14, 1999, pp. 1191-1199.
[28] N. K. Suryadevara and S. C. Mukhopadhyay, “Determining Wellness Through An Ambient Assisted Living Environment”, IEEE Intelligent Systems, May/June 2014, pp. 30-37.
[29] I. W. Tsang, J. T. Kwok and P. M. Cheung, “Core vector machines: Fast SVM training on very large data sets”, Journal of Machine Learning Research, Vol. 6, 2005, pp. 363-392.
[30] N.K. Suryadevara and S.C. Mukhopadhyay, “Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly”, IEEE Sensors Journal, Vol. 12, No. 6, June 2012, pp. 1965-1972.
[31] S. Asharaf, M. N. Murty and S. K. Shevade, “Multiclass Core Vector Machine”, Proc. the 24th International Conference on Machine Learning (ICML 2007), 2007, pp. 41-48.
[32] Francesco Orabona, “Better Algorithms for Selective Sampling”, Proc. the 28th International Conference on Machine Learning (ICML 2011), 2011, pp. 433-440.
[33] Y. Liu, “Combining integrated sampling with SVM ensembles for learning from imbalanced datasets”, Information Processing and Management, Vol. 47, No. 4, 2011.
[34] M. Volpi, “Memory-Based Cluster Sampling for Remote Sensing Image Classification”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 50, No. 8, 2012.
[35] G. Sen Gupta, S.C. Mukhopadhyay and M Finnie, Wi-Fi Based Control of a Robotic Arm with Remote Vision, Proceedings of 2009 IEEE I2MTC Conference, Singapore, May 5-7, 2009, pp. 557-562.
[36] Abhimanyu Das and David Kempe, “Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection”, Proc. the 28th International Conference on Machine Learning (ICML 2011), 2011, pp. 1057-1064.
[37] G.Sengupta, T.A.Win, C.Messom, S.Demidenko and S.C.Mukhopadhyay, “Defect analysis of grit-blasted or spray printed surface using vision sensing technique”, Proceedings of Image and Vision Computing NZ, Nov. 26-28, 2003, Palmerston North, pp. 18-23.
[38] Y. Q. Wang, Y. Li, and L. Chang, “Approximate Minimum Enclosing Ball Algorithm with Smaller Core Sets for Binary Support Vector Machine”, Proc. the 2010 Chinese Control and Decision Conference (CCDC 2010), 2010, pp. 3404-3408.

EXTRA FILES

COMMENTS