CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 10033 > Article
Paper
29 August 2016 A real time vision system for traffic surveillance at intersections
Juan Li, Qinglian He, Liya Yang, Chunfu Shao
Author Affiliations +
Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100335N (2016) https://doi.org/10.1117/12.2244920
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China
Abstract
Traffic data collected at intersections are essential information for traffic signal operations, traffic control, and intersection design and planning. Compared with highway traffic detections, traffic surveillance at intersections has more challenges due to the variety of road users and weaving caused by traffic conflicts. One of these problems is the detection failure of stopping road users. The other challenge is to track objects during occlusion caused by traffic conflicts. In this study, a real time video surveillance system is developed to detect, track and classify road users at intersections. At first, an improved Gaussian Mixture Model (GMM) is utilized to detect road users, including temporary stopping objects due to traffic conflicts. Then, a motion estimation approach is used to get the trajectories of road users. Finally, the Back Propagation Neural Network (BPNN) is employed to classify pedestrians, bicycles, and vehicles. Experimental results show that the proposed traffic surveillance system is effective and successful for road user detection, tracking and identification at intersections.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Juan Li, Qinglian He, Liya Yang, and Chunfu Shao "A real time vision system for traffic surveillance at intersections", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100335N (29 August 2016); https://doi.org/10.1117/12.2244920
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
 5 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Roads
Surveillance
Motion detection
Motion models
Video surveillance
Detection and tracking algorithms
Expectation maximization algorithms
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top