IEEE Trans Pattern Anal Mach Intell. 2018 Jul;40(7):1697-1710. doi: 10.1109/TPAMI.2017.2726061. Epub 2017 Jul 12.
Gait recognition is an important topic in biometrics. Current works primarily focus on recognizing a single person's walking gait. However, a person's gait will change when they walk with other people. How to recognize the gait of multiple people walking is still a challenging problem. This paper proposes an attribute discovery model in a max-margin framework to recognize a person based on gait while walking with multiple people. First, human graphlets are integrated into a tracking-by-detection method to obtain a person's complete silhouette. Then, stable and discriminative attributes are developed using a latent conditional random field (L-CRF) model. The model is trained in the latent structural support vector machine (SVM) framework, in which a new constraint is added to improve the multi-gait recognition performance. In the recognition process, the attribute set of each person is detected by inferring on the trained L-CRF model. Finally, attributes based on dense trajectories are extracted as the final gait features to complete the recognition. The experimental results demonstrate that the proposed method achieves better recognition performance than traditional gait recognition methods under the condition of multiple people walking together.
步态识别是生物识别领域的一个重要课题。目前的工作主要集中在识别单个人的行走步态。然而，当一个人与其他人一起行走时，他们的步态会发生变化。如何识别多个人的步态仍然是一个具有挑战性的问题。本文提出了一种在最大边缘框架下的属性发现模型，用于在与多个人一起行走时基于步态识别一个人。首先，将人体图嵌入到一个基于检测的跟踪方法中，以获得一个人的完整轮廓。然后，使用潜在条件随机场（L-CRF）模型开发稳定和有区别的属性。该模型在潜在结构支持向量机（SVM）框架中进行训练，其中添加了一个新的约束来提高多步态识别性能。在识别过程中，通过对训练好的 L-CRF 模型进行推断来检测每个人的属性集。最后，提取基于密集轨迹的属性作为最终的步态特征来完成识别。实验结果表明，与传统的步态识别方法相比，该方法在多个人一起行走的情况下具有更好的识别性能。
