These methods have good robustness to large view changes, but they often require expensive and complex configurations of calibrated high-resolution multi-camera systems with extensive computation and frame synchronization, all of which limit their application to real surveillance scenarios. For 3D gait information methods, the gait information is extracted from multi-view gait videos and used to construct 3D gait models. Specifically, four cross-view gait recognition methods are analyzed from the perspective of feature representation and classification, i.e., 3D gait information construction, view transformation model (VTM), view-invariant feature extraction, and the deep learning-based methods. Unlike most existing review papers that classify gait recognition methods by the basic steps such as data acquisition, feature representation and classification, this paper focuses on methods that solve the cross-view recognition problem. Then, cross-view gait classification methods are carefully presented. On this basis, the popular cross-view gait databases are sorted from data type, sample size, view-point number, acquisition environment and other covariates, and the characteristics of these databases are analyzed in detail. Then the focus of this paper is drawn to the review of video-based cross-view gait recognition methods. Firstly, we briefly introduce the research background of the field from the perspectives of basic concepts, data acquisition methods, application scenarios, development history and existing reviews. Based on extensive research, this paper provides a review of existing cross-view gait recognition methods. Therefore, improving the robustness of cross-view gait recognition has become a hot topic with a high research and application significance. The intra-class differences of different view-points are often greater than the inter-class differences of the same view-point. In these practical applications, the performance of gait recognition is easily affected by covariates such as view-point variations, occlusions and segmentation error, among which view-point variations are one of the main factors affecting the gait recognition performance. Based on the above unique advantages, gait recognition has a broad application prospect in security monitoring, investigation and evidence collection, and daily attendance. Moreover, one’s gait is difficult to be hidden or disguised. Compared with face recognition, fingerprint recognition, iris recognition and other biometric methods, gait recognition can be performed at a distance and does not require special acquisition equipment, high image resolution, or subject cooperation. Gait recognition aims at determining a person"s identity by the way he or she walks.
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