Each person describes unique patterns during gait cycles and this information can be extracted from live video stream and used for subject identification. In recent years, there has been a profusion of sensors that in addition to RGB video images also provide depth data in real-time. In this paper, a method to enhance the appearance-based gait recognition method by also integrating features extracted from depth data is proposed. Two approaches are proposed that integrate simple depth features in a way suitable for real-time processing. Unlike previously presented works which usually use a short range sensors like Microsoft Kinect, here, a long-range stereo camera in outdoor environment is used. The experimental results for the proposed approaches show that recognition rates are improved when compared to existing popular gait recognition methods.

中文翻译：

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基于深度的实时步态识别

每个人在步态周期中描述独特的模式，并且可以从实时视频流中提取此信息并用于主题识别。近年来，除了 RGB 视频图像外，还有大量传感器可以实时提供深度数据。在本文中，提出了一种通过整合从深度数据中提取的特征来增强基于外观的步态识别方法的方法。提出了两种以适合实时处理的方式集成简单深度特征的方法。与之前展示的作品通常使用 Microsoft Kinect 等短程传感器不同，这里使用了室外环境中的远程立体摄像头。所提出方法的实验结果表明，与现有流行的步态识别方法相比，识别率有所提高。