Pedestrian detection based on I-HOG feature

Yongjun Zhang; Yongjie Zou; Haisheng Fan; Wenjie Liu; Zhongwei Cui

doi:10.1117/12.2607200

28 October 2021 Pedestrian detection based on I-HOG feature

Yongjun Zhang, Yongjie Zou, Haisheng Fan, Wenjie Liu, Zhongwei Cui

Proceedings Volume 11884, International Symposium on Artificial Intelligence and Robotics 2021; 118841V (2021) https://doi.org/10.1117/12.2607200
Event: International Symposium on Artificial Intelligence and Robotics 2021, 2021, Fukuoka, Japan

Abstract

Pedestrian detection is a hot and difficult topic in the computer vision field. The Histograms of Oriented Gradients (HOG) feature, because of its high performance in accuracy, is widely used in pedestrian detection. Nonetheless, its information description capacity needs further improvement. so, I-HOG (Improved HOG) was proposed. I-HOG has two major improvements. First, I-HOG enhances the description of edge features. Through the different scales for the block histograms of a set of correlation graphs, makes the correlation between characteristic information. Second, I-HOG using multi-scale feature extraction methods, include wider edge feature description information, make up for the deficiencies of the HOG feature, because HOG features are only extracted in fixed block size, The experimental results show that in the INRIA database, using I-HOG, detection rate increased by 5.4% and 4.3% respectively, combined with the feature of CSS after detection rate increased by 2.8% and 4.0% respectively compared to the HOG.

Citation Download Citation

Yongjun Zhang, Yongjie Zou, Haisheng Fan, Wenjie Liu, and Zhongwei Cui "Pedestrian detection based on I-HOG feature", Proc. SPIE 11884, International Symposium on Artificial Intelligence and Robotics 2021, 118841V (28 October 2021); https://doi.org/10.1117/12.2607200

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