A double-region learning algorithm for counting the number of pedestrians in subway surveillance videos

Abstract

Counting pedestrians in surveillance videos has become an urgent safety concern in critical areas. However, surveillance videos of subway spaces suffer from severe crowd occlusion and perspective distortion. In this paper, a novel double-region learning algorithm is presented to overcome these challenges. The main idea of this algorithm is to identify the best two-region boundary and then design a reasonable pedestrian-counting method in each separated region. First, a separate line is obtained via possibility learning, and each frame is divided into a nearby region and a distant region to eliminate the influence of perspective distortion. Second, in the nearby region, we apply the improved aggregate channel feature detection to count the number of pedestrians N1. In the distant region, we employ the Extreme Learning Machine and Gaussian Process regression methods to estimate the number of pedestriansN2. Finally, the total number of pedestrians in each frame can be obtained with high accuracy according to N1 and N2. We establish a subway pedestrian video dataset about several typical subway stations in Shanghai to validate the algorithm performance. Various experimental results demonstrate that the accuracy of the proposed approach surpasses that of compared methods, which means that our algorithm can meet the management requirements of subway stations.