基于深度测量的行人体态特征提取与再识别方法

行人再识别是视觉监控系统的核心问题之一，然而传统基于彩色图像的特征提取方法难以用于极暗光照条件下的行人再识别。本文提出一种基于深度测量的行人体态特征提取方法，由于深度测量独立于光照条件，因此所提方法可以在极暗光照条件下对行人目标进行有效识别。由深度数据经过分割和滤波生成人体点云，将观测点云与初始人体模型进行配准，基于配准后的点云对人体模型的体态参数和姿态参数进行联合估计，计算体态特征向量的欧式距离实现行人再识别。在公开数据集和实验室自采数据集上进行验证，计算Rank-n、累计匹配曲线、平均精度均值等性能指标，其中在Single shot评估模式下BIWI数据集的Rank-1可达到70.71%、Rank-5可达到92.32%,结果表明本文所提算法可有效提高再识别精度。

Person shape feature extraction and reidentification based on depth measurement

Person re-identification is a fundamental problem in the smart video surveillance system. However, the traditional RGB-based feature extraction method cannot be used in dark environment. A new method for person shape feature extraction using depth measurement is proposed in this article. The depth data are independent from lighting condition. Therefore, the proposed method can be used for person re-id in the dark. Specifically, the point cloud of person is generated from depth data after segmentation and filtering. Then, the point cloud is registered to the initial human body model. The shape and pose parameters of the body model are estimated jointly based on the registered point cloud. Finally, the re-id is achieved by calculating the Euclidean distance in the vector space of shape parameters. The author applies this method on public and self-collected datasets in the laboratory to calculate performance indicators, including Rank-n, cumulative matching curve, and mean average precision, etc. Among the indicators, the Rank-1 of BIWI datasets in Single shot evaluation mode reaches 70. 71% and the Rank-5 of BIWI datasets is up to 92. 32% , which indicate that the proposed algorithm can effectively improve the re-recognition accuracy.