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Moving object detection method based on SIFT (Scale Invariant Feature Transform) feature matching

A feature matching and moving target technology, applied in the field of computer vision, can solve the problems of low stability, no real-time performance, and high time complexity of moving target detection, achieving less time complexity, ensuring real-time performance and robustness , the effect of fast detection speed

Inactive Publication Date: 2016-09-28
BEIHANG UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to solve the shortcomings of high time complexity, no real-time performance and low stability in the detection of moving objects in dynamic scenes

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  • Moving object detection method based on SIFT (Scale Invariant Feature Transform) feature matching
  • Moving object detection method based on SIFT (Scale Invariant Feature Transform) feature matching
  • Moving object detection method based on SIFT (Scale Invariant Feature Transform) feature matching

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Embodiment Construction

[0019] The technical solutions of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0020] The invention proposes a moving target detection method based on SIFT feature matching, aiming at reducing the time complexity of traditional target detection algorithms, improving the real-time performance of target detection, and increasing the robustness of target detection.

[0021] The implementation steps are described in detail below.

[0022] Step 1: SIFT feature extraction. Obtain the feature point set of the initial frame of the image sequence.

[0023] Step 1.1, construct the scale space.

[0024] The two-dimensional Gaussian function is defined as follows:

[0025] G ( x , y , σ ) = 1 2 πσ 2 ...

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Abstract

The invention discloses a moving object detection method based on SIFT (Scale Invariant Feature Transform) feature matching. The method comprises steps: an SIFT feature extraction method is firstly used for acquiring feature points of the image; quick and accurate matching is then carried out on the SIFT feature points of the image; a global moving model is built according to features of a dynamic scene, an improved RANSAC (Random Sample Consensus) method is used for excluding influences from an external point, a least square method is used for solving a global moving parameter, the moving parameters are updated timely according to feature point change, an updating strategy based on a residual image block is used for updating, and a second nearest neighbor search area restricting method is used for ensuring the accuracy of feature matching; and finally, a differential target segmentation method is used for realizing detection on a moving target. An experiment proves that compared with the traditional image block-based matching detection method, the method improves the computing speed by 31.26%, background interference can be effectively eliminated, the detected target image is distinct, and the method is particularly applicable to real-time detection on the moving target in the dynamic scene.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a moving target detection method based on SIFT (Scale Invariant Feature Transform, scale invariant feature transformation) feature matching. Background technique [0002] Moving target detection is to judge whether there is a target in the image and determine the position of the moving target, which is the basis of computer vision. Fast and accurate target detection can pave the way for subsequent target tracking and behavior understanding. As camera technology improved, fixed cameras were replaced by rotatable cameras. The camera moves and rotates, causing the background and objects in the image to move at the same time. At the same time, illumination changes and background interference also increase the difficulty of moving target detection. Traditional object detection algorithms such as frame difference method, optical flow method and background difference method are not suit...

Claims

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Application Information

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IPC IPC(8): G06T7/20G06K9/46
CPCG06T2207/10016G06V10/462
Inventor 艾明晶刘锐
Owner BEIHANG UNIV
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