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Method for tracking target based on graph theory cluster and color invariant space

A color-invariant, target tracking technology, applied in the field of target tracking based on graph theory clustering and color-invariant space, can solve the problems of mismatching of different moving objects, limited SIFT, and large amount of matching calculation.

Active Publication Date: 2011-11-09
NANJING HUICHUAN IND VISUAL TECH DEV
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Problems solved by technology

[0005] The problem to be solved by the present invention is: in the existing local invariant feature extraction and matching research, SIFT is limited to grayscale images, cannot process color images, and its matching calculations are large, and there are sometimes false matches between different moving objects. occur

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  • Method for tracking target based on graph theory cluster and color invariant space
  • Method for tracking target based on graph theory cluster and color invariant space
  • Method for tracking target based on graph theory cluster and color invariant space

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Embodiment Construction

[0042] The present invention is based on the target tracking method of graph theory clustering and color invariant space, extracts feature points, then performs graph theory clustering on the movement trends of these points, classifies feature points into respective targets, and performs Track matches such as figure 1 , including the following processes:

[0043] 1) Carry out color-invariant space conversion to the image of the video stream, use color-invariant feature CSIFT feature extraction, detect and extract color-invariant and scale-invariant features, and calculate invariant feature vectors;

[0044] 2) Graph theory motion clustering of features: Based on graph theory, cluster the feature points of video frames according to the feature motion trend, take the current frame as the reference image, the next frame is the image to be matched, and use the CSIFT features of these two frames Points are used as the nodes of the graph to obtain the motion trend information of ea...

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Abstract

The invention relates to a method for tracking a target based on graph theory cluster and color invariant space. The method provided by the invention comprises the following steps of: (1) carrying out color invariant space transformation on an image of a video flow; extracting feature points by utilizing color invariant features CSIFT (Colored Scale Invariant Feature Transform); (2) carrying out graph theory movement cluster on the features, wherein the feature points with the same movement tendency belong to targets with the same movement state; and tracking targets in a video frame by the feature points. In the invention, local invariant features are utilized and the blindness of overall features is abandoned; the extraction of the color invariant features CSIFT and increases the colorinvariance property is realized while the geometrical invariability advantages of the SIFT features are kept so that a gray level feature space is advanced to the color space; to a movable object under a static background, the graph theory through is matched and a feature movement classification is used as the pre-treatment of matching so that the matching operation is more accurate and faster.

Description

technical field [0001] The invention belongs to the technical field of video image processing, relates to target tracking in video, and is a target tracking method based on graph theory clustering and color invariant space. Background technique [0002] Object tracking has a very wide range of research and applications in the fields of visual navigation, behavior recognition, intelligent transportation and so on. Most of the current motion detection and target tracking algorithms are based on Harris and SIFT features, but it is difficult to solve problems such as matching efficiency and color changes caused by low light differences. Other tracking algorithms use luminance constancy to compute optical flow, however if the intensity distribution is relatively uniform, such as walls with less prominent features, or if it has a one-dimensional intensity distribution, such as edges, the image rate cannot be reliably estimated. [0003] The extraction and matching of image featur...

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Application Information

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IPC IPC(8): G06T7/20G06K9/62
Inventor 李勃陈抒瑢董蓉翟霄宇顾昊丁文杨娴陈启美郁建桥徐亮
Owner NANJING HUICHUAN IND VISUAL TECH DEV
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