Crowd Estimation Using Multi-Scale Local Texture Analysis and Confidence-Based Soft Classification
Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium(2008)
摘要
Crowd estimation is crucial for crowd monitoring and control. It differs from pedestrian detection or people counting in that no individual pedestrian can be properly segmented in the image. This paper describes a novel and efficient system for crowd density estimation, based on local image texture analysis. A novel indication of local binary pattern feature vector called Advanced LBP is proposed and adopted as multi-scale texture descriptor, which exhibits high distinctive power. Confidence-based soft classifier gives more reasonable crowd estimates. Experiment results from real crowded scene videos demonstrate the performance and potential of our method.
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关键词
crowd estimation,local image texture analysis,pedestrian detection,local binary pattern feature,novel indication,crowd monitoring,confidence-based soft classification,multi-scale texture descriptor,reasonable crowd estimate,individual pedestrian,multi-scale local texture analysis,crowd density estimation,estimation,image texture,accuracy,testing,density estimation,histograms,feature vector,computer vision,local binary pattern,image classification,feature extraction
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