Multi-Target Tracking Based on Regional Correlation and Color Histogram Match

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Abstract:

A method based on regional correlation and color histogram match was proposed for multi-target adaptive tracking. Firstly, target detection method was used to get the difference images. Then, the same target could be recognized according to the correlation of target regions of consecutive frames and the matching rate of color histogram. Finally, the active value, lifetime and dormancy value of targets were used to carry out long-time target tracking and deal with the problems of missing detection, false detection and target exit. The experiment shows that the method proposed in this paper has reached to a high accuracy rate of 95%, which has good robustness against the processing of missing detection, false detection and target exit.

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Periodical:

Advanced Materials Research (Volumes 998-999)

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631-637

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July 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Faliang Chang, Xue Liu, Huajie Wang. Target tracking algorithm based on meanshift and Kalman filter. Computer Engineering and Applications, 2007, 43(12): 50-52. (In Chinese).

Google Scholar

[2] Xiaolin Zhao, Hui Wu, Liguo Sun. Non-rigid object tracking based on active basis model. Journal of Image and Graphics, 2011, 16(10): 1826-1831. (In Chinese).

Google Scholar

[3] Juan Zhang, Xiaobo Mao, Tiejun Chen. Survey of moving object tracking algorithm. Application Research of Computers, 2009, 26(12): 4407-4410. (In Chinese).

Google Scholar

[4] C. Stauffer, W.E.L. Grimson. Learning patterns of activity using real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 747-757.

DOI: 10.1109/34.868677

Google Scholar

[5] P. Kadewtrakupong, R. Bowden. An improved adaptive background mixture model for real-time tracking with shadow detection[Z]. London, U. K: Kluwer Academic Publishers, 2001: 1, 1-5.

Google Scholar

[6] Shouhua Yu, Jihong Chen, Jingying Ou. Study on the detection method of pigs in piglet pigsty based on the characteristics of pigs. Advanced Materials Research, 2012, 403-408: 2271-2276.

DOI: 10.4028/www.scientific.net/amr.403-408.2271

Google Scholar