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| Visual Tracking Algorithm Combining ORB Feature and Color Model |
| ZHONG Hua-Min, WANG Wei, ZHANG Hui-Hua |
Key Laboratory of System Control and Information Processing of Ministry of Education of China,
Department of Automation, Shanghai Jiao Tong University, Shanghai 200240 |
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Abstract To solve the problem of invalid tracking by traditional CAMShift owing to the background with similar colors, a dynamic visual tracking algorithm is proposed combining ORB feature and color model of the object. The ORB feature is applied to extract the initial position of the object, and a adaptive color-threshold segmentation algorithm is proposed to improve the accuracy of color model for the object. Besides, the information of ORB feature points is used to revise the search window in the tracking procedure, which improves the tracking accuracy and robustness. Furthermore, a new method is proposed to estimate whether the moving object is missing, and an iteratively updated feature template is built to relocate the disappeared target. The experiments on video sequence images demonstrate that the proposed algorithm outperforms CAMShift and other improved algorithms based on feature extraction. When the target moves at high speed, the proposed algorithm has good robustness and can find out the wrong tracking result and correct it. Moreover, the computational efficiency rises greatly to ensure the real-time performance.
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Received: 12 August 2013
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[1] Ohlmeyer E J, Menon P K. Applications of the Particle Filter for Multi-object Tracking and Classification // Proc of the American Control Conference. Washington, USA, 2013: 6181-6186
[2] Niknejad H T, Takeuchi A, Mita S, et al. On-Road Multivehicle Tracking Using Deformable Object Model and Particle Filter with Improved Likelihood Estimation. IEEE Trans on Intelligent Transportation Systems, 2012, 13(2): 748-758
[3] Quast K, Kaup A. Shape Adaptive Mean Shift Object Tracking Using Gaussian Mixture Models // Adami N, Cavallaro A, Leonardi R, et al. Analysis, Retrieval and Delivery of Multimedia Content. New York, USA: Springer-Verlag, 2013: 107-122
[4] Beyan C, Temizel A. Adaptive Mean-Shift for Automated Multi Object Tracking. IET Computer Vision, 2012, 6(1): 1-12
[5] Babenko B, Yang M H, Belongie S. Visual Tracking with Online Multiple Instance Learning // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA, 2009: 983-990
[6] Kalal Z, Mikolajczyk K, Matas J. Tracking-Learning-Detection. IEEE Trans on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1409-1422
[7] Zhang K H, Zhang L, Yang M H. Real-Time Compressive Tracking // Proc of the 12th European Conference on Computer Vision. Flo-rence, Italy, 2012, III: 864-877
[8] Bradski G R, Clara S. Computer Vision Face Tracking for Use in a Perceptual User Interface. Intel Technology Journal, 1998, Q2: 1-15
[9] Exner D, Bruns E, Kurz D, et al. Fast and Robust CAMShift Tracking // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. San Francisco, USA, 2010: 9-16
[10] Sun K, Liu S R. Combined Algorithm with modified Camshift and Kalman Filter for Multi-object Tracking. Information and Control, 2009, 38(1): 9-14 (in Chinese)
(孙 凯,刘士荣.多目标跟踪的改进Camshift/卡尔曼滤波组合算法.信息与控制, 2009, 38(1): 9-14
[11] Qiu X N, Liu S R, Liu F. An Adaptive Kernel-Based Target Tracking Method Based on Multiple Features Fusion. IEEJ Transactions on Electrical and Electronic Engineering, 2012, 7(1): 91-97
[12] Lowe D G. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 2004, 60(2): 91-110
[13] Zhao Y J, Pei H L, Zhou H B. Improved Vision-Based Algorithm for Unmanned Aerial Vehicles Autonomous Landing. Journal of Multimedia, 2013, 8(2): 90-97
[14] Bay H, Ess A, Tuytelaars T, et al. Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding, 2008, 110(3): 346-359
[15] Rublee E, Rabaud V, Konolige K, et al. ORB: An Efficient Alternative to SIFT or SURF // Proc of the IEEE International Conference on Computer Vision. Barcelona, Spain, 2011: 2564-2571
[16] Rosten E, Porter R, Drummond T. Faster and Better: A Machine Learning Approach to Corner Detection. IEEE Trans on Pattern Analysis and Machine Intelligence, 2010, 32(1): 105-119
[17] Calonder M , Lepetit V, Strecha C, et al. BRIEF: Binary Robust Independent Elementary Features // Proc of the 11th European Conference on Computer Vision. Heraklion, Greece, 2010, IV: 778-792
[18] Fischler M A, Bolles R C. Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM, 1981, 24(6): 381-395 |
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2015, Vol. 28 
