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Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 7, Issue 4, Pages 1,516-1,534, DOI: https://doi.org/10.21307/ijssis-2017-718
License : (CC BY-NC-ND 4.0)
Received Date : 15-August-2014 / Accepted: 21-October-2014 / Published Online: 01-December-2014
In recent years, due to the video surveillance applications more and more widely, people are
not satisfied with the goal of monitoring, and the video monitoring technology of intelligent video
moving object detection and tracking technology has received extensive attention. The research work in
this paper is in the field, the moving target detection spatiotemporal correlation and difference contour
tracking algorithm based on a fixed background. The algorithm in the background under the condition
of fixed to pay a smaller time complexity, the target detection and tracking has a good effect, so it has
higher application value. In this paper, the prospect of caused motion detection of occlusion
background foreground correlation problem, put forward the video moving object detection method
based on kernel independent component analysis, canonical correlation to minimize the component in
the high dimensional feature space in order to separate the foreground nuclear background.
Independent component analysis assumes that the foreground and background independent, avoid the
correlation problem. The two objective functions based on Kernel Independent Component Analysis:
analysis of kernel independent component analysis based on kernel canonical component (KCCA) and
kernel independent component analysis (KGV) based on the generalized variance. KCCA is the
application of canonical correlation analysis in the kernel method, discuss is the first canonical
correlation separation component of high dimensional map, and KGV are typical correlation between
the components in the high dimensional space of the whole spectrum. Both KCCA and KGV improved
the accuracy of motion detection
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