Multiple Properties-Based Moving Object Detection Algorithm


Changjian Zhou, Jinge Xing, Haibo Liu, Journal of Information Processing Systems Vol. 17, No. 1, pp. 124-135, Feb. 2021  

10.3745/JIPS.02.0153
Keywords: Moving object detection, Multiple Properties, SIFT Vector Field
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Abstract

Object detection is a fundamental yet challenging task in computer vision that plays an important role in object recognition, tracking, scene analysis and understanding. This paper aims to propose a multiproperty fusion algorithm for moving object detection. First, we build a scale-invariant feature transform (SIFT) vector field and analyze vectors in the SIFT vector field to divide vectors in the SIFT vector field into different classes. Second, the distance of each class is calculated by dispersion analysis. Next, the target and contour can be extracted, and then we segment the different images, reversal process and carry on morphological processing, the moving objects can be detected. The experimental results have good stability, accuracy and efficiency.


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Cite this article
[APA Style]
Changjian Zhou, Jinge Xing, & Haibo Liu (2021). Multiple Properties-Based Moving Object Detection Algorithm. Journal of Information Processing Systems, 17(1), 124-135. DOI: 10.3745/JIPS.02.0153.

[IEEE Style]
C. Zhou, J. Xing and H. Liu, "Multiple Properties-Based Moving Object Detection Algorithm," Journal of Information Processing Systems, vol. 17, no. 1, pp. 124-135, 2021. DOI: 10.3745/JIPS.02.0153.

[ACM Style]
Changjian Zhou, Jinge Xing, and Haibo Liu. 2021. Multiple Properties-Based Moving Object Detection Algorithm. Journal of Information Processing Systems, 17, 1, (2021), 124-135. DOI: 10.3745/JIPS.02.0153.