论文标题
人通过分析步态序列的动态变化来重新识别
Person Re-identification by analyzing Dynamic Variations in Gait Sequences
论文作者
论文摘要
步态识别是一种生物识别技术，它通过分析步行或肢体运动风格来识别视频顺序的个人。但是，这种识别通常对外观变化和常规特征描述符（例如步态能量图像（GEI））敏感，丢失了步态序列中的某些动态信息。与GEI相比，主动能量图像（AEI）更多地关注动态运动变化，并且更适合处理外观变化。我们提出了一种新方法，该方法允许通过分析动态运动变化并识别人们而无需使用预测更改的数据库来识别人们。在提出的方法中，通过平均剪影序列的差帧并分为多个段来计算活动能量图像。仿射力矩不变性被计算为每个部分的步态特征。接下来，根据提取的功能与数据库中的功能之间的相似性计算匹配权重。最后，通过所有段中相似性的加权组合来确定受试者。 CASIA-B步态数据库用作实验分析的主要数据集。
Gait recognition is a biometric technology that identifies individuals in a video sequence by analysing their style of walking or limb movement. However, this identification is generally sensitive to appearance changes and conventional feature descriptors such as Gait Energy Image (GEI) lose some of the dynamic information in the gait sequence. Active Energy Image (AEI) focuses more on dynamic motion changes than GEI and is more suited to deal with appearance changes. We propose a new approach, which allows recognizing people by analysing the dynamic motion variations and identifying people without using a database of predicted changes. In the proposed method, the active energy image is calculated by averaging the difference frames of the silhouette sequence and divided into multiple segments. Affine moment invariants are computed as gait features for each section. Next, matching weights are calculated based on the similarity between extracted features and those in the database. Finally, the subject is identified by the weighted combination of similarities in all segments. The CASIA-B Gait Database is used as the principal dataset for the experimental analysis.
