结合特征图谱学习的人数统计方法

针对实际公共场景视频的人数统计中存在的背景干扰、光照变化、目标间遮挡等问题，提出一种结合特征图谱学习和一阶动态线性回归的人数统计方法。首先，建立图像的尺度不变特征变换（SIFT）特征与目标真实密度图之间的特征图谱映射模型，利用SIFT特征和前述映射模型得到包含目标和背景特征量的特征图谱；然后，根据通常监控视频中背景变化较小、特征图谱中的背景特征量相对稳定的特点，由特征图谱的积分与真实人数通过一阶动态线性回归建立人数回归模型；最后，通过该回归模型模型得出估计人数。在数据集MALL和PETS2009上进行实验，实验结果表明：与累积属性空间方法相比，所提方法平均绝对误差降低了2.2%；与基于角点检测的一阶动态线性回归方法相比，其平均绝对误差降低了6.5%，平均相对误差降低了2.3%。

People counting method combined with feature map learning

In order to solve the problems such as background interference, illumination variation and occlusion between targets in people counting of actual public scene videos, a new people counting method combined with feature map learning and first-order dynamic linear regression was proposed. Firstly, the mapping model of feature map between the Scale-Invariant Feature Transform (SIFT) feature of image and the target true density map was established, and the feature map containing target and background features was obtained by using aforementioned mapping model and SIFT feature. Then, according to the facts of the less background changes in the monitoring video and the relatively stable background features in the feature map, the regression model of people counting was established by the first-order dynamic linear regression from the integration of feature map and the actual number of people. Finally, the estimated number of people was obtained through the regression model. The experiments were performed on the datasets of MALL and PETS2009. The experimental results show that, compared with the cumulative attribute space method, the mean absolute error of the proposed method is reduced by 2.2%, while compared with the first-order dynamic linear regression method based on corner detection, the mean absolute error and the mean relative error of the proposed method are respectively reduced by 6.5% and 2.3%.