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Personnel indoor positioning method based on adaptive SIFI (scale invariant feature transform) algorithm

An indoor positioning and self-adaptive technology, applied in the field of image processing, can solve problems such as increased algorithm complexity, inability to take advantage of algorithm advantages, and high algorithm time overhead

Inactive Publication Date: 2012-08-08
BEIJING UNIV OF TECH
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Problems solved by technology

Although the SIFT algorithm has strong performance, it also leads to a sharp increase in the complexity of the algorithm. To extract features from a 320×240 image, a total of 600 feature points are determined, which takes 1.1364 seconds. The original algorithm takes too much time. Large, directly applied to the indoor positioning system cannot give full play to the advantages of the algorithm itself, and does not meet the real-time requirements

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  • Personnel indoor positioning method based on adaptive SIFI (scale invariant feature transform) algorithm
  • Personnel indoor positioning method based on adaptive SIFI (scale invariant feature transform) algorithm
  • Personnel indoor positioning method based on adaptive SIFI (scale invariant feature transform) algorithm

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Embodiment Construction

[0033] The present invention will be further described below in conjunction with the drawings and specific implementations. The implementation process includes the following steps:

[0034] 1) Execute the SIFT algorithm on the reference frame image, and store all the detected feature points in the sequence F.

[0035] 2) According to the matching times, choose to use linear interpolation or Lagrange parabolic interpolation to predict the overlapping area of ​​the reference frame and the current frame image, and execute the SIFT algorithm on this area, and store all the detected feature points in the sequence S.

[0036] 3) Perform feature matching on sequence F and sequence S with Euclidean distance, and use RANSAC algorithm to eliminate mismatches, and finally get the correct set of matching points.

[0037] 4) If there are more than 3 matching points, use the obtained matching points as a sample set for parameter model estimation; otherwise, go to step 1.6.

[0038] 5) The obtained tr...

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Abstract

The invention provides a personnel indoor positioning method based on an adaptive SIFI (scale invariant feature transform) algorithm. The method is characterized by: firstly, carrying out feature extraction on an global motion image sequence; then, taking a correct matching point as a sample set to estimate a camera motion model so as to obtain an actual offset of a camera. In the method of the invention, a Lagrange parabola interpolation is introduced. Last three times of model matching results are used to predict an overlapping region of a reference frame image and a current frame image. On the overlapping region, feature points are extracted and feature matching is performed so that a lot of information redundancies in a video image sequence can be eliminated, a processing speed of the each frame image can be accelerated, the validity of the feature points to be matched can be increased and mismatching can be reduced. Therefore, the algorithm is accurate and real-time and can be used for the indoor personnel positioning system.

Description

Technical field [0001] The invention belongs to the field of image processing. It is a method of estimating global motion by computer technology, image capturing technology and digital image processing technology to realize the automatic positioning of indoor personnel. This method realizes the automatic analysis of the video motion image sequence, finds out its motion law, and determines the specific location of the target. Background technique [0002] Global motion is a motion mode caused by changes in camera position or parameters. It includes motion-based scene analysis, understanding, and three-dimensional motion analysis. It is currently mainly used for video coding, mobile robot visual navigation, target tracking and recognition Wait. Estimating the two-dimensional parameter model of the global motion video sequence image is the global motion estimation, and its purpose is to finally restore the motion state of the camera through the analysis of the motion state of the ...

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Application Information

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IPC IPC(8): G06K9/64
Inventor 张会清安健逞曹鲁光邓贵华
Owner BEIJING UNIV OF TECH
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