Flow Mosaicking: Real-Time Pedestrian Counting Without Scene-Specific Learning

Abstract

In this paper, we present a novel algorithm based on flow velocity field estimation to count the number of pedestrians across a detection line or inside a specified region. We regard pedestrians across the line as fluid flow, and design a novel model to estimate the flow velocity field. By integrating over time, the dynamic mosaics are constructed to count the number of pixels and edges passed through the line. Consequentially, the number of pedestrians can be estimated by quadratic regression, with the number of weighted pixels and edges as input. The regressors are learned off line from several camera tilt angles, and have taken the calibration information into account. We use tilt-angle-specific learning to ensure direct deployment and avoid overfitting while the commonly used scene-specific learning scheme needs on-site annotation and always trends to overfitting. Experiments on a variety of videos verified that the proposed method can give accurate estimation under different camera setup in real-time.