The critical goal of gait recognition is to acquire the inter-frame walking habit representation from the gait sequences. The relations between frames, however, have not received adequate attention in comparison to the intra-frame features. In this paper, motivated by optical flow, the bilateral motion-oriented features are proposed, which can allow the classic convolutional structure to have the capability to directly portray gait movement patterns at the feature level. Based on such features, we develop a set of multi-scale temporal representations that force the motion context to be richly described at various levels of temporal resolution. Furthermore, a correction block is devised to eliminate the segmentation noise of silhouettes for getting more precise gait information. Subsequently, the temporal feature set and the spatial features are combined to comprehensively characterize gait processes. Extensive experiments are conducted on CASIA-B and OU-MVLP datasets, and the results achieve an outstanding identification performance, which has demonstrated the effectiveness of the proposed approach.

翻译：动作识别的关键目标是从动作序列中获得跨框架行走习惯的描述。但是,与机体内部特征相比,各框架之间的关系没有得到足够的重视。在本文件中,以光学流动为动机,提出了双边运动导向的特征,使典型的演进结构能够直接描述地貌水平的行走模式。根据这些特征,我们制定了一套多尺度的时间描述,迫使运动背景在时间分辨率的不同层次上得到丰富的描述。此外,还设计了一个校正块,以消除沙发的分离噪音,以获得更精确的音频信息。随后,将时间特征集和空间特征组合起来,全面描述行走过程。对CASIA-B和U-MVLP数据集进行了广泛的实验,结果取得了杰出的识别表现,显示了拟议方法的有效性。