Continuously Adaptive Mean-shift Tracking Algorithm with Suppressed Background Histogram Model

Xudong WANG; Yiwei WANG; He YAN

doi:10.11999/JEIT180588

Volume 41 Issue 6

Jun. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(6): 1480-1487

Xudong WANG, Yiwei WANG, He YAN. Continuously Adaptive Mean-shift Tracking Algorithm with Suppressed Background Histogram Model[J]. Journal of Electronics & Information Technology, 2019, 41(6): 1480-1487. doi: 10.11999/JEIT180588

Citation:

Xudong WANG, Yiwei WANG, He YAN. Continuously Adaptive Mean-shift Tracking Algorithm with Suppressed Background Histogram Model[J]. Journal of Electronics & Information Technology, 2019, 41(6): 1480-1487. doi: 10.11999/JEIT180588

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Continuously Adaptive Mean-shift Tracking Algorithm with Suppressed Background Histogram Model

doi: 10.11999/JEIT180588

Xudong WANG^{1
,
,},
Yiwei WANG²,
He YAN¹

1.
College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2.
8511 Institution of China Aerospace Science and Industry Corporation, Nanjing 210007, China

Funds: Aviation fund (20182007001, 2017052015)

Received Date: 2018-06-13
Rev Recd Date: 2019-03-08

Available Online: 2019-03-27

Publish Date: 2019-06-01

Abstract

Abstract

For the deficiency of traditional Continuously Adaptive Mean-shift (CAMshift) tracking algorithm can easily contain a large number of color information which belongs to the background in the process of establishing the target color model, an improved algorithm is proposed. The original image is divided into foreground and background based on the Gaussian Mixture Model(GMM). In the original image and the background image, the histogram of the hue component is established. Hue histograms of the background image are used to calculate the weight of the hue component in the original image. The hues belonging to the background are suppressed and the color differences between foreground and background are expanded. Experiment shows that by suppressing the hue components belonging to the background, the saliency of the target color model is expanded. The accuracy and stability of the target recognition are improved. The ratio of the max deviation to the target is less than 20%, which ensures the target not to be lost.
- Target tracking,
- Background suppression,
- Histogram,
- Hue,
- Continuously Adaptive Mean-shift (CAMshift) tracking algorithm

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References(15)

References

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