Abstract
The appearance of inexpensive high-quality video cameras and depth cameras resulted in the development of a large number of object-tracking algorithms. In this paper, a new descriptor-based algorithm for real-time object tracking using the information from a Microsoft Kinect depth camera is proposed. As a descriptor for the object tracked, histograms of oriented gradients calculated from the circular sliding regions of the scene image are used. The information on the depth of the scene is used when the image of the object of interest is partially occluded by other objects in the scene. To speed up the tracking process, a model for predicting the object motion is used. To ensure real-time tracking with the proposed algorithm, a multicore graphics processor is used.
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References
A. Yilmaz, O. Javed, and M. Shah, “Object tracking: A survey,” ACM Comput. Surv. 38, 45 (2006).
H. Schweitzer, J. Bell, and F. Wu, “Very fast template matching,” in Proc. 7th Eur. Conf. on Comput. Vis. (ECCV 2002), Copenhagen, Denmark, May 28–31, 2002 (Springer-Verlag, 2002), pp. 358–372.
S. Nejhum, J. Ho, and M. Yang, “Online visual tracking with histograms and articulating blocks,” in Comput. Vis. Image Underst., 2010, pp. 901–914.
V. H. Díaz-Ramírez, K. Picos, and V. Kober, “Target tracking in nonuniform illumination conditions using locally adaptive correlation filters,” Opt. Commun. 323, 32–43 (2014).
I. Haritaoglu, D. Harwood, and L. Davis, “W4: realtime surveillance of people and their activities,” IEEE Trans. Pattern. Anal. Mach. Intell. 22, 809–830 (2000).
F. Talu, I. Turkoglu, and M. Cebeci, “A hybrid tracking method for scaled and oriented objects in crowded scenes,” Expert Syst. Appl. 38, 13682–13687 (2011).
A. Buchanan and A. Fitzgibbon, “Document image dewarping using robust estimation of curled text lines,” Combining local and global motion models for feature point tracking, in Comput. Vision Pat. Recogn., 2007, pp. 1–8.
I. Sbalzarini and P. Koumoutsakos, “Feature point tracking and trajectory analysis for video imaging in cell biology,” J. Struct. Biol. 151 182–195 (2005).
Z. Kalal, K. Mikolajczyk, and J. Matas, “Trackinglearning- detection,” IEEE Trans. Pattern. Anal. Mach. Intell. 34, 1409–1422 (2012).
B. Babenko, Y. C. Ming-Hsuan, and S. Belongie, “Visual tracking with online multiple instance learning,” in Proc. IEEE Conf. on Comput. Vision and Patt. Rec. (CVPR 2009), Miami, Florida, June 20–25, 2009, (IEEE, New York, 2009), pp. 983–990.
S. Song and J. Xiao, “Cluster based weighted SVM for the recognition of Farsi handwritten digits,” in Tracking Revisited Using Rgbd Camera: Unified Benchmark and Base-Lines, (2013), pp. 233–240.
K. Meshgi, S. Maeda, S. Oba, and S. Ishii, “Fusion of multiple cues from color and depth domains using occlusion aware bayesian tracker,” IEICE Tech. Rep. Neurocomp. 114 (500), 127–132 (2014).
N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” Comput. Vis. Patt. Rec. 1, 886–893 (2005).
D. Miramontes-Jaramillo, V. Kober, and V. Díaz-Ramírez, “CWMA: Circular window matching algorithm,” in Proc. 18th Iberoam. Cong. in Patt. Rec., 2013, LNCS 8258, pp. 439–446 (2013).
D. Miramontes-Jaramillo, V. I. Kober, V. H. Díaz-Ramírez, and V. N. Karnaukhov, “A novel Image Matching Algorithm Based on Sliding Histograms of Oriented Gradients,” J. Commun. Technol. Electron. 59, 1446–1450 (2014).
V. Gupta, Nonlinear Filters with Spatially Connected Neighborhoods (Laxmi Publications, 2005).
E. M. Ramos and V. Kober, “Design of correlation filters for recognition of linearly distorted objects in linearly degraded scenes,” J. OSA A 24, 3403–3417 (2007).
L. P. Yaroslavsky and M. Eden, Fundamentals of Digital Optics (Birkhäuse, Boston, 1996).
L. Po-Ming and C. Hung-Yi, “Adjustable gamma correction circuit for TFT LCD,” in Proc. IEEE Symp. on Circ. and Syst., 2005 (IEEE, New York, 2005), pp. 780–783.
W. K. Pratt, Digital Image Processing (Wiley, 2007).
G. Takacs, V. Chandrasekhar, S. Tsai, R. Grzeszczuk, and B. Girod, “Distortion invariant pattern recognition with local correlations,” Fast Computation of Rotation- Invariant Image Features by Approximate Radial Gradient Transform, 22, No. 8, pp. 2970–2982 (2013).
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Original Russian Text © D. Miramontes-Jaramillo, V.I. Kober, V.H. Díaz-Ramírez, V.N. Karnaukhov, 2016, published in Informatsionnye Protsessy, 2016, Vol. 16, No. 2, pp. 13–26.
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Miramontes-Jaramillo, D., Kober, V.I., Díaz-Ramírez, V.H. et al. Descriptor-based tracking algorithm using a depth camera. J. Commun. Technol. Electron. 62, 638–647 (2017). https://doi.org/10.1134/S1064226917060146
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DOI: https://doi.org/10.1134/S1064226917060146