We propose a new vision-based people-counting method that uses flow analysis with the movement speed of a person to increase the accuracy of people-counting. The proposed method consists of two procedures: simple estimation of foreground movement speed and multiple people detection based on the flow analysis. First, we extract the flow that is generated by the movements of the foreground, and its volume that is calculated by accumulating the foreground pixels on a line of interest (LOI) while people enter and exit the target region. Second, the number of frames containing the foreground in the LOI for each entry and exit event is counted to estimate the speed of the flow cluster. Finally, the number of people is estimated from the flow volume (FV) and the number of frames. In the experimental results, the proposed method enhanced the average F1 score and accuracy by up to 25% and 9%, respectively, compared to existing people-counting methods. The results confirmed that the proposed method achieved substantial accuracy improvements over existing methods when the person passed the target region for various speed patterns.

我们提出了一种新的基于视觉的人数统计方法，该方法使用流量分析和人的移动速度来提高人数统计的准确性。所提出的方法包括两个过程：前景移动速度的简单估计和基于流分析的多人检测。首先，我们提取由前景运动产生的流，其体积是通过在人们进入和离开目标区域时在兴趣线 (LOI) 上累积前景像素来计算的。其次，计算每个进入和退出事件的 LOI 中包含前景的帧数，以估计流簇的速度。最后，根据流量（FV）和帧数估计人数。在实验结果中，与现有的人数统计方法相比，所提出的方法将平均 F1 分数和准确率分别提高了 25% 和 9%。结果证实，当人通过各种速度模式的目标区域时，所提出的方法比现有方法取得了显着的精度提高。