Paper Info
Title
Nighttime Traffic Flow Analysis for Rain-Drop Tampered Cameras
Abstract
The proposed system provides a solution to analyze the traffic flow under challenging nighttime conditions when the surveillance camera is raindrop tampered. To deal with the challenging scenes, we extract effective features via salient region detection and block segmentation. We use the extracted features in the region of interest to construct a regression model to get an estimated vehicle number for each frame. The vehicle numbers in consecutive frames form a vehicle number sequence. A mapping model utilizing state transition likelihoods is proposed to acquire the desired per minute traffic flow from the vehicle number sequence. The experiments on highly challenging datasets have demonstrated that the proposed system can effectively estimate the traffic flow for rain-drop tampered highway surveillance cameras at night.
Year
DOI
Venue
2014
10.1109/ICPR.2014.133
ICPR
Keywords
Field
DocType
intelligent,traffic flow analysis,traffic engineering computing,regression analysis,highway surveillance camera,highway,image segmentation,regression model,consecutive frame,surveillance,salient region detection,feature extraction,cameras,object detection,state transition likelihood,regression,rain-drop tampered camera,mapping model,intelligent, surveillance, highway, traffic flow analysis, regression,road traffic,block segmentation,vehicle number sequence,nighttime traffic flow analysis,video surveillance
Computer vision,Traffic flow,Computer science,Segmentation,Feature extraction,Artificial intelligence,Region of interest,Region detection,Hidden Markov model,Salient,Traffic flow analysis
Conference
ISSN
Citations 
PageRank 
1051-4651
0
0.34
References 
Authors
10
2
Name
Order
Citations
PageRank
Hsu-Yung Cheng124323.56
Chih-Chang Yu2328.93