HOG pedestrian detection based on edge symmetry and trilinear interpolation

Dandan Wang; Tongei Lu; Yanduo Zhang

doi:10.1117/12.2282815

8 March 2018 HOG pedestrian detection based on edge symmetry and trilinear interpolation

Dandan Wang, Tongei Lu, Yanduo Zhang

Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 1060907 (2018) https://doi.org/10.1117/12.2282815
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

Abstract

In computer vision, pedestrian detection is a key problem. In this paper, we propose to speed up the HOG+SVM algorithm without sacrificing the classification accuracy. In order to eliminate the effects of aliasing phenomenon that products in the process of HOG extraction, we used trilinear interpolation to extract feature. This paper proposed HOG pedestrian detection method based on edge symmetry. In these experiments, we used INRIA dataset. Traditional HOG pedestrian detection is presence of slow detection speed and low detection rate. Experiments show that using trilinear interpolation and edge symmetry not only can improve the detection effect, but also can improve the detection rate.

Citation Download Citation

Dandan Wang, Tongei Lu, and Yanduo Zhang "HOG pedestrian detection based on edge symmetry and trilinear interpolation", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 1060907 (8 March 2018); https://doi.org/10.1117/12.2282815

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available