Gray level target tracking algorithm based on marginal information and mean shift

A mean shift and edge information technology, applied in the field of image processing, can solve problems such as being easily affected by changes in background gray levels and tracking failures

Inactive Publication Date: 2010-12-15
BEIHANG UNIV
View PDF6 Cites 30 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although these methods improve the performance of the tracking algorithm to some extent, when the texture or size of the ta...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Gray level target tracking algorithm based on marginal information and mean shift
  • Gray level target tracking algorithm based on marginal information and mean shift
  • Gray level target tracking algorithm based on marginal information and mean shift

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0086] The technical solution of the present invention will be described in further detail below through specific examples.

[0087] The tracking algorithm of the present invention is used to track the gray-scale image sequence of the aircraft whose gray-scale distribution, shape size and background gray-scale of a group of targets change. The image size is 314×240, and the target size changes from 78×71 to 18 ×9, the initial frame image such as figure 2 As shown, the red window marked is the initial target; the original image sequence is as follows image 3 shown.

[0088] Step 1: Preprocess the grayscale target image.

[0089] (1) Use the image smoothing method to remove the noise of the gray image. Different smoothing methods can be used according to the different types of noise. Here, the spatial neighborhood averaging method with 3×3 windows is selected to smooth the image.

[0090] (2) In order to better extract the edge information of the target, the histogram contr...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a gray level target tracking algorithm based on marginal information and mean shift, which comprises the following steps: (1) preprocessing a gray level target image, including image de-noising, contrast enhancement and Sobel filtration; (2) extracting a feature template of the target in a preprocessed initial frame image; (3) forecasting an initial position of the target in the current frame by using a Kalman filter; (4) searching near the initial position forecasted by the Kalman filter in a mean shift tracking algorithm and obtaining an optimal position of the target in the current frame; and (5) updating the target template in combination with Canny filtration at regular intervals. Based on the marginal information and the mean shift algorithm, effective tracking of the gray level target is realized, and the tracking can be steadily finished in real time when the size, shape, gray level distribution and background of the target change.

Description

(1) Technical field: [0001] The invention relates to a grayscale target tracking algorithm, which belongs to the technical field of image processing. (two) background technology: [0002] Target tracking has been widely used in research fields such as computer vision, surveillance systems, civil security inspection and infrared guidance. The essence of target tracking is to determine the position and geometric information of the target in the image sequence. Compared with color targets, grayscale targets contain less image information, move more irregularly, and are easily occluded or interfered by the background, which greatly increases the difficulty of grayscale target tracking and has become a research hotspot in the field of computer vision. [0003] Object tracking methods are divided into two categories: one is based on the motion of the object; the other is based on the characteristics of the object. Since the tracking algorithm based on target features has better ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06T7/20
Inventor 毛峡常乐刁伟鹤
Owner BEIHANG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap