Fast People Counting Using Sampled Motion Statistics

In this paper, we proposed a fast people counting method using the sampled motion statistics. Unlike most traditional approaches uses tracking methods to monitor the moving target until passing through the gate (or region), our method only analyzed motion information in the region of interest to achieve bi-directional people counting. By setting a variety of parameters, a sampled image area according to the pre-defined object size is established in the prior calibration phase. Combining the motion features and its directional status in image sequences, people counts can be estimated through spatial-temporal analysis. Without complex object labeling and tracking procedures, our method shows robust performance in outdoor environments and can be easily implemented on embedded systems. The experimental results show that the proposed method can achieve high accuracy of 94.95% and rapid processing frame rate over 100fps.