A New Adaptive Bidirectional Region-of-Interest Detection Method for Intelligent Traffic Video Analysis

Document Type

Conference Proceeding

Publication Date

12-1-2020

Abstract

Real-time intelligent video-based traffic surveillance applications play an important role in intelligent transportation systems. To reduce false alarms as well as to increase computational efficiency, robust road segmentation for automated Region of Interest (RoI) detection becomes a popular focus in the research community. A novel Adaptive Bidirectional Detection (ABD) of region-of-interest method is presented in this paper to automatically segment the roads with bidirectional traffic flows into two regions of interest. Specifically, a foreground segmentation method is first applied along with the flood-fill algorithm to estimate the road regions. Then the Lucas-Kanade's optical flow algorithm is utilized to track and divide the estimated road into regions of interest in real-time. Experimental results using a dataset of real traffic videos illustrate the feasibility of the proposed method for automatically determining the RoIs in real-time.

Identifier

85102400266 (Scopus)

ISBN

[9781728187082]

Publication Title

Proceedings 2020 IEEE 3rd International Conference on Artificial Intelligence and Knowledge Engineering Aike 2020

External Full Text Location

https://doi.org/10.1109/AIKE48582.2020.00012

First Page

17

Last Page

24

Grant

1647170

Fund Ref

National Science Foundation

This document is currently not available here.

Share

COinS