Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Pedestrian detection method based on binary image improved HOG characteristics

A technology of pedestrian detection and binary image, which is applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problems of large memory consumption and slow detection speed, and achieve the effect of improving accuracy and reducing storage space consumption

Active Publication Date: 2017-08-11
SOUTH CHINA UNIV OF TECH
View PDF5 Cites 13 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art, and provide a pedestrian detection method based on the improved HOG feature of binary images, which can effectively solve the shortcomings of large memory consumption and slow detection speed in the process of pedestrian detection

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
  • Pedestrian detection method based on binary image improved HOG characteristics
  • Pedestrian detection method based on binary image improved HOG characteristics
  • Pedestrian detection method based on binary image improved HOG characteristics

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0046] The present invention will be further described in detail below in conjunction with the embodiments and the accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0047] A pedestrian detection method based on the improved HOG feature of the binary image, such as figure 1 , including the following steps:

[0048] Learning phase:

[0049] S1. Establish a pedestrian training sample library.

[0050] The selection of the pedestrian training sample library follows the following two rules:

[0051] Rule 1: The ratio of pedestrian negative samples to pedestrian positive samples is 10:1;

[0052]Rule 2: Use the SVM trained for the first time to detect misdetected negative samples in the negative samples as difficult negative samples, and increase the proportion of these difficult negative samples, thereby further improving the accuracy of the established model.

[0053] S2. Perform binarization processing on the sample image.

[0054...

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 pedestrian detection method based on binary image improved HOG characteristics. The method comprises steps that a pedestrian training sample database is established; binary processing on sample images is carried out; improved HOG characteristic vectors of the binary images are extracted; Gauss normalization for the improved HOG characteristic vectors is carried out, positive and negative samples are utilized for training to acquire various parameters of an SVM model, and a linear SVM model is established; to-be-detected video frame images are pre-processed to acquire binary images; the present improved HOG characteristic vectors are acquired through calculation; the improved HOG characteristic vectors are inputted to the linear SVM model, if model output is determined to be a positive sample, a target is detected, and the target position is outputted; whether each traversal window has the target is detected in a window traversal mode. The method is advantaged in that problems of large memory consumption and slow detection speed in a pedestrian detection process can be effectively solved.

Description

technical field [0001] The invention relates to the field of computer vision pedestrian detection, in particular to a pedestrian detection method based on binary image improved HOG features. Background technique [0002] At present, pedestrian detection methods are mainly divided into two categories: template matching-based methods and statistical learning-based methods. Among them, the pedestrian detection algorithm based on statistical learning has gradually been widely used because the diversity of statistical algorithms can meet a variety of applications. [0003] In the method based on statistical learning, the Histogram of oriented gradient (HOG) feature is a very effective gradient feature, which has good robustness to image pedestrian detection under illumination changes and color changes. This method first divides the image into small connected regions, which are defined as cell units, and collects the gradient or edge direction histogram of each pixel in the cell ...

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): G06K9/00G06K9/38G06K9/46
CPCG06V40/103G06V10/28G06V10/50
Inventor 冯颖杨涛苏比哈什·如凯迦陈新开
Owner SOUTH CHINA UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com