Paper Info
Title
Accurate Traffic Flow Estimation for Highway Surveillance Systems with Scenes Tampered by Raindrops
Abstract
A traffic flow estimation mechanism is proposed for highway surveillance systems with scenes tampered by raindrops. To detect rain-drop tampered scenes, features are extracted via salient region detection and block segmentation. Feature selection is performed to select more discriminative features. For traffic flow estimation, daytime and night time models are performed separately to adapt to the characteristics of the surveillance scenes. Finally, an effective graph-based mapping method is designed to map the vehicle count sequences to per minute traffic flow. The system is tested with a highly challenging dataset. The accuracy of the traffic flow analysis is satisfying with low mean absolute errors even when the cameras are seriously tampered by rain.
Year
DOI
Venue
2019
10.1109/AVSS.2019.8909906
2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
Keywords
Field
DocType
Highway Surveillance,Tampered Scene,Feature Extraction,Feature Selection,Traffic Parameter Estimation
Computer vision,Traffic flow,Computer science,Artificial intelligence
Conference
ISSN
ISBN
Citations 
2643-6205
978-1-7281-0991-6
0
PageRank 
References 
Authors
0.34
24
2
Name
Order
Citations
PageRank
Hsu-Yung Cheng124323.56
Chih-Chang Yu200.68