一种基于归一化前景和角点信息的复杂场景人数统计方法

A Method for People Counting in Complex Scenes Based on Normalized Foreground and Corner Information

针对智能视频监控领域的人数统计问题,该文提出了一种基于归一化前景和角点信息的复杂场景人数统计方法。首先在提取的前景二值图基础上,计算透视校正后的归一化前景面积。然后在提取前景区域有效角点信息的基础上,计算能够反映人群遮挡程度的遮挡因子。最后,将上述两种特征输入后向传播(BP)网络完成人数统计算法的训练与测试。实验表明,该方法可以有效地实现对复杂场景的人数统计。

For the problem of people counting in intelligent video surveillance, a method of people counting in complex scenes based on the normalized foreground and corner information is proposed. First, based on the binary foreground, the area of normalized foreground after perspective correction is calculated. Second, the optimized corner information of foreground is extracted to compute the occlusion coefficient of crowd. Finally, the above two features are used as the inputs of the Back Propagation （BP） neural network to train and test the people counting. Experiments results show that, the proposed method exhibits good performance in complex scenes.