Open Access
ARTICLE
Pedestrian Crossing Detection Based on HOG and SVM
Yunzuo Zhang*, Kaina Guo, Wei Guo, Jiayu Zhang, Yi Li
School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China
* Corresponding Author: Yunzuo Zhang. Email:
Journal of Cyber Security 2021, 3(2), 79-88. https://doi.org/10.32604/jcs.2021.017082
Received 25 January 2021; Accepted 24 May 2021; Issue published 02 August 2021
Abstract
In recent years, pedestrian detection is a hot research topic in the field
of computer vision and artificial intelligence, it is widely used in the field of
security and pedestrian analysis. However, due to a large amount of calculation
in the traditional pedestrian detection technology, the speed of many systems for
pedestrian recognition is very limited. But in some restricted areas, such as
construction hazardous areas, real-time detection of pedestrians and cross-border
behaviors is required. To more conveniently and efficiently detect whether there
are pedestrians in the restricted area and cross-border behavior, this paper
proposes a pedestrian cross-border detection method based on HOG (Histogram
of Oriented Gradient) and SVM (Support Vector Machine). This method extracts
the moving target through the GMM (Gaussian Mixture Model) background
modeling and then extracts the characteristics of the moving target through
gradient HOG. Finally, it uses SVM training to distinguish pedestrians from nonpedestrians, completes the detection of pedestrians, and labels the targets. The
test results show that only the HOG feature extraction of the candidate area can
greatly reduce the amount of calculation and reduce the time of feature extraction,
eliminate background interference, thereby improving the efficiency of detection,
and can be applied to occasions with real-time requirements.
Keywords
Cite This Article
Y. Zhang, K. Guo, W. Guo, J. Zhang and Y. Li, "Pedestrian crossing detection based on hog and svm,"
Journal of Cyber Security, vol. 3, no.2, pp. 79–88, 2021.