复杂背景灰度图像下的多特征融合运动目标跟踪

江山; 张锐; 韩广良; 孙海江

doi:10.3788/CO.20160903.0320

Volume 9 Issue 3

May 2016

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Article Contents

Chinese Optics > 2016 > 9(3): 320-328.

JIANG Shan, Zhang Rui, HAN Guang-liang, SUN Hai-jiang. Moving object tracking based on multi-feature fusion in the complex background gray image[J]. Chinese Optics, 2016, 9(3): 320-328. doi: 10.3788/CO.20160903.0320

Citation:

JIANG Shan, Zhang Rui, HAN Guang-liang, SUN Hai-jiang. Moving object tracking based on multi-feature fusion in the complex background gray image[J]. Chinese Optics, 2016, 9(3): 320-328. doi: 10.3788/CO.20160903.0320

Citation:

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Moving object tracking based on multi-feature fusion in the complex background gray image

doi: 10.3788/CO.20160903.0320

Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China

Funds:

Supported by National Natural Science Foundation of China No.61172111

Received Date: 23 Nov 2015
Rev Recd Date: 18 Jan 2016
Publish Date: 25 Jan 2016

Abstract

Abstract

In order to solve the problem of current moving object tracking algorithm which can not apply in some non-ideal conditions such as low contrast, low signal to noise ratio, target rotation and scaling, this paper presents a method based on multi-feature fusion in the complex background by improving the meanshift algorithm to realize the complex gray image tracking. The algorithm needs to not only meet the conditions required for engineering practice but also satisfy precise in object tracking stabilization. Firstly, using the algorithm we calculate gradient characteristics in gray image, the gradient characteristics including gradient features in X, Y two directions. Secondly, the algorithm integrates the two directions gradient and gray features to get new fusion features. The new fusion features provide more distinguishable measurements than the traditional ones, and they have high adaptability and stability in some conditions such as low contrast, large flexible changes of targets by using the kernel density function. Thirdly, the foreground objects results can be extracted by background modeling object detection algorithm, which takes moving target feature information as a weight value. Finally, the fusion features object is tracked by improved meanshift algorithm in this paper. A series of experiments results show that the multi-feature fusion moving object tracking method can stably track low contrast target in complex gray image. The algorithm can adapt to the complex changes of object and background. And it also has strong robustness to meet the actual needs of the engineering practice.
- target tracking,
- edge detection,
- sobel operator,
- MeanShift

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References(19)

References

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