Crowd Estimation Using Multi-Scale Local Texture Analysis and Confidence-Based Soft Classification

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.