Title
Raindrop-Tampered Scene Detection and Traffic Flow Estimation for Nighttime Traffic Surveillance
Abstract
In this paper, we propose an intelligent highway surveillance system that performs self-diagnosis and detects conditions when the camera is seriously tampered by raindrops at night. The system also provides solutions to analyze the traffic flow under the challenging nighttime raindrop-tampered conditions. To deal with the challenging scenes, we extract effective features via salient region detection and block segmentation. The extracted features are used to train a support vector machine to achieve self-diagnosis. For traffic flow analysis, we use the extracted features in the region of interest and construct a regression model to get an estimated vehicle count for each frame. The vehicle counts in consecutive frames form a vehicle count sequence. We propose a mapping model to acquire the desired per-minute traffic flow from the vehicle count sequence. The model utilizes state transfer likelihoods and takes into account the length of the segmented vehicle count sequence. The experiments on highly challenging data sets have demonstrated that the proposed system can effectively estimate the traffic flow for raindrop-tampered highway surveillance cameras at night.
Year
DOI
Venue
2015
10.1109/TITS.2014.2365033
Intelligent Transportation Systems, IEEE Transactions  
Keywords
Field
DocType
raindrop-tampered camera,regression,salient regions,traffic flow analysis,regression analysis,support vector machine,night,support vector machines,image segmentation,traffic flow,intelligent transportation systems,feature extraction,regression model,estimation
Computer vision,Data set,Traffic flow,Simulation,Segmentation,Regression analysis,Support vector machine,Artificial intelligence,Region of interest,Engineering,Region detection,Salient
Journal
Volume
Issue
ISSN
16
3
1524-9050
Citations 
PageRank 
References 
2
0.37
23
Authors
3
Name
Order
Citations
PageRank
Chih-Chang Yu1328.93
Hsu-Yung Cheng224323.56
Yi-Fan Jian320.37