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Moving Object Refining in Traffic Monitoring Applications
Kunfeng Wang; Qingming Yao; Xin Qiao; Shuming Tang; Fei-Yue Wang
Conference NameThe Proceedings of the 10th IEEE International Conference on Intelligent Transportation Systems
Source PublicationIntelligent Transportation Systems Conference
Conference Date30 Sept.-3 Oct. 2007
Conference PlaceSeattle, WA, USA
AbstractMoving object segmentation is an important task in vision-based traffic monitoring applications. In traffic scenes, various outliers such as sudden illumination changes, moving cast shadows, camera jitter, etc., often cause serious errors in image analysis due to misclassification of moving objects. An efficient moving object refining approach is thus expected. In this paper, we address the problem of moving object refining by processing the background subtraction results. In an analytical multi-stage procedure, we remove sudden illumination changesandlocalreflectedregionsemployingphotometriccolor invariants, remove moving cast shadows based on a single Gaussian shadow model, uniform-region classification, and spatial analysis, and further remove other types of outliers in a postprocessing stage of area filtering and area-to-perimeter test. Experimental results on actual video sequences representative of different traffic scenes and illuminations are presented. The results illustrate that our approach is efficient when handling widely different conditions that can occur.
KeywordGaussian Processes Computerised Monitoring Filtering Theory Image Classification Image Colour Analysis Image Segmentation Image Sequences Lighting Object Detection Photometry
Document Type会议论文
Corresponding AuthorShuming Tang
Recommended Citation
GB/T 7714
Kunfeng Wang,Qingming Yao,Xin Qiao,et al. Moving Object Refining in Traffic Monitoring Applications[C],2007.
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