CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 12079 > Article
Paper
1 December 2021 Pedestrian passage and height detection system based on deep learning
Jinjiang Cao, Fei Ren, Libing Xu, Hongsheng Li, Jitian Qian, Aiping Hu
Author Affiliations +
Proceedings Volume 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering; 120791E (2021) https://doi.org/10.1117/12.2622725
Event: 2nd IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 2021, Xi'an, China
Abstract
Object detection is an important research direction in the field of machine vision, which is widely used in industry, security and intelligent transportation. In the intelligent traffic gate detection, most of the visual detection is concentrated on face detection, which can not detect the behavior of people who follow or walk side by side and their height. In order to solve this problem, we proposed a visual detection algorithm based on Yolo V3 deep learning network model, and trained the network model for pedestrian detection; The tracking algorithm based on Yolo V3 sort adopts two-line detection, which can not only detect the following, side-by-side and other abnormal behaviors, but also effectively solve the problem of single line detection; At the same time, the target pedestrian is segmented by using Yolo V3 grabcut, and the segmented image is filtered and binarized. After traversing the pixels, the height detection is realized based on the optical axis aggregation model. The experimental results show that the improved network model realizes the detection of ticket evasion behavior such as tailing, side by side and height detection, improves the accuracy of pedestrian detection, and can meet the needs of intelligent transportation field.
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Jinjiang Cao, Fei Ren, Libing Xu, Hongsheng Li, Jitian Qian, and Aiping Hu "Pedestrian passage and height detection system based on deep learning", Proc. SPIE 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 120791E (1 December 2021); https://doi.org/10.1117/12.2622725
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
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
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 10 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Detection and tracking algorithms
Target detection
Image segmentation
Cameras
Image processing algorithms and systems
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top