Raindrop-tampered scene detection and traffic flow estimation for nighttime traffic surveillance

Chih Chang Yu, Hsu Yung Cheng, Yi Fan Jian

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish
Article number6953115
Pages (from-to)1518-1527
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number3
StatePublished - 1 Jun 2015


  • Raindrop-tampered camera
  • regression
  • salient regions
  • traffic flow analysis


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