Raindrop-Tampered Scene Detection and Traffic Flow Estimation for Nighttime Traffic Surveillance

In this paper, the authors 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, the authors 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, the authors 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. The authors 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.

Language

  • English

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 01574985
  • Record Type: Publication
  • Files: TLIB, TRIS
  • Created Date: Aug 5 2015 12:32PM