College of Automation Science and Engineering, South China University of Technology, Guangzhou, China.
Neural Netw. 2012 Nov;35:92-102. doi: 10.1016/j.neunet.2012.07.012. Epub 2012 Aug 27.
Recognition of temporal/dynamical patterns is among the most difficult pattern recognition tasks. Human gait recognition is a typical difficulty in the area of dynamical pattern recognition. It classifies and identifies individuals by their time-varying gait signature data. Recently, a new dynamical pattern recognition method based on deterministic learning theory was presented, in which a time-varying dynamical pattern can be effectively represented in a time-invariant manner and can be rapidly recognized. In this paper, we present a new model-based approach for human gait recognition via the aforementioned method, specifically for recognizing people by gait. The approach consists of two phases: a training (learning) phase and a test (recognition) phase. In the training phase, side silhouette lower limb joint angles and angular velocities are selected as gait features. A five-link biped model for human gait locomotion is employed to demonstrate that functions containing joint angle and angular velocity state vectors characterize the gait system dynamics. Due to the quasi-periodic and symmetrical characteristics of human gait, the gait system dynamics can be simplified to be described by functions of joint angles and angular velocities of one side of the human body, thus the feature dimension is effectively reduced. Locally-accurate identification of the gait system dynamics is achieved by using radial basis function (RBF) neural networks (NNs) through deterministic learning. The obtained knowledge of the approximated gait system dynamics is stored in constant RBF networks. A gait signature is then derived from the extracted gait system dynamics along the phase portrait of joint angles versus angular velocities. A bank of estimators is constructed using constant RBF networks to represent the training gait patterns. In the test phase, by comparing the set of estimators with the test gait pattern, a set of recognition errors are generated, and the average L(1) norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. Therefore, the test gait pattern similar to one of the training gait patterns can be rapidly recognized according to the smallest error principle. Finally, experiments are carried out on the NLPR and UCSD gait databases to demonstrate the effectiveness of the proposed approach.
时间/动态模式识别是最困难的模式识别任务之一。人体步态识别是动态模式识别领域中的一个典型难题。它通过时变步态特征数据对个体进行分类和识别。最近，提出了一种基于确定性学习理论的新的动态模式识别方法，其中可以有效地以时不变的方式表示时变动态模式，并可以快速识别。在本文中，我们提出了一种基于上述方法的新的基于模型的人体步态识别方法，特别是通过步态来识别人。该方法包括两个阶段：训练（学习）阶段和测试（识别）阶段。在训练阶段，选择侧轮廓下肢关节角度和角速度作为步态特征。采用五连杆人体步态运动模型，证明包含关节角度和角速度状态向量的函数表征了步态系统动力学。由于人体步态的准周期性和对称性特点，步态系统动力学可以简化为单侧人体关节角度和角速度的函数来描述，从而有效地降低了特征维度。通过确定性学习，使用径向基函数（RBF）神经网络（NN）实现了对步态系统动力学的局部精确识别。所获得的近似步态系统动力学知识存储在常数 RBF 网络中。然后从提取的步态系统动力学沿关节角度与角速度的相图中导出步态特征。使用常数 RBF 网络构建了一个估计器库，以表示训练步态模式。在测试阶段，通过将估计器集合与测试步态模式进行比较，生成一组识别误差，并将误差的平均 L(1)范数作为训练步态模式动力学和测试步态模式动力学之间的相似性度量。因此，可以根据最小误差原则快速识别与训练步态模式之一相似的测试步态模式。最后，在 NLPR 和 UCSD 步态数据库上进行实验，以验证所提出方法的有效性。
