Abstract
Gait recognition using noninvasively acquired data has been attracting an increasing interest in the last decade. Among various modalities of data sources, it is experimentally found that the data involving skeletal representation are amenable for reliable feature compaction and fast processing. Model-based gait recognition methods that exploit features from a fitted model, like skeleton, are recognized for their view and scale-invariant properties. We propose a model-based gait recognition method, using sequences recorded by a single flash lidar. Existing state-of-the-art model-based approaches that exploit features from high quality skeletal data collected by Kinect and Mocap are limited to controlled laboratory environments. The performance of conventional research efforts is negatively affected by poor data quality. We address the problem of gait recognition under challenging scenarios, such as lower quality and noisy imaging process of lidar, that degrades the performance of state-of-the-art skeleton-based systems. We present GlidarCo to attain high accuracy on gait recognition under the described conditions. A filtering mechanism corrects faulty skeleton joint measurements, and robust statistics are integrated to conventional feature moments to encode the dynamic of the motion. As a comparison, length-based and vector-based features extracted from the noisy skeletons are investigated for outlier removal. Experimental results illustrate the efficacy of the proposed methodology in improving gait recognition given noisy low resolution lidar data.
Abstract (translated)
近十年来，利用非侵入性获取的数据进行步态识别的兴趣越来越大。在各种数据源形式中，实验发现，涉及骨架表示的数据易于进行可靠的特征压缩和快速处理。基于模型的步态识别方法利用拟合模型的特征，如骨架，由于它们的视图和尺度不变性而得到识别。我们提出了一种基于模型的步态识别方法，该方法使用一个闪光激光雷达记录的序列。利用Kinect和Mocap收集的高质量骨骼数据特征的现有最先进的基于模型的方法仅限于受控的实验室环境。传统研究工作的绩效受到数据质量差的负面影响。针对激光雷达成像质量差、成像过程噪声大等复杂情况下的步态识别问题，对现有的基于骨架的系统性能进行了研究。为了在所描述的条件下获得高精度的步态识别，我们提出了滑翔机。一个过滤机制纠正了错误的骨骼关节测量，并将鲁棒统计信息集成到常规特征矩中，对运动的动态进行编码。作为比较，研究了从噪声骨架中提取的基于长度的特征和基于向量的特征，以便去除异常值。实验结果表明，该方法在噪声低分辨率激光雷达数据下改善步态识别的效果。
URL
https://arxiv.org/abs/1905.07058
