基于深度测量的行人体态特征提取与再识别方法

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.