Contributions to Gait Recognition Using Multiple-Views

Q4 Computer Science
D. López-Fernández
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引用次数: 0

Abstract

This thesis focuses on identifying people by the way they walk. The problem of gait recognition has been addressed by using different approaches, both in the 2D and 3D domains, and using one or multiple views. However, the dependence on camera viewpoint (and therefore the dependence on the trajectory of motion) still remains an open problem. This dissertation addresses the problem of dependence on the trajectory through the use of 3D reconstructions of walking humans. The use of 3D models have several advantages that are worth mentioning. First, by the use of 3D reconstructions it is possible to exploit a greater amount of information in contrast to methods that extract descriptors from just 2D images. Second, the 3D reconstructions can be aligned along the way as if the subject had walked on a treadmill, thus providing a way to recognize people regardless the path. Three approaches are proposed in order to address the dependence on the trajectory: (1) using aligned 3D reconstructions of walking humans, (2) using unaligned 3D reconstructions of walking humans. (3) extracting a 3D description without using 3D reconstructions. Three gait descriptors are also proposed. The first focuses on describing gait by means of morphological analysis of 3D aligned volumes. The second makes use of the concept of entropy to describe the dynamics of human gait. The third aims to capture the dynamics of gait in a rotation invariant way, which makes it interesting for recognize people walking on both straight and curves path, and regardless direction changes. These approaches have been tested on the "AVA Multi-View Dataset (AVAMVG)" and on the "Kyushu University 4D Gait Database (KY4D)". Both databases are specifically designed to address the problem of dependence on the viewpoint, and therefore the dependence on the trajectory. Experimental results show that for the approach based on aligned volumetric reconstructions, the entropy-based gait descriptor achieved the best results compared to other closely related methods of the state-of-art. However, the rotation invariant gait descriptor achieves a recognition rate that overcomes the compared state-of-art methods without requiring the alignment of the 3D gait reconstructions.
基于多视角的步态识别研究
这篇论文的重点是通过人们走路的方式来识别他们。步态识别问题已经通过在2D和3D域中使用不同的方法以及使用一个或多个视图来解决。然而,对摄像机视点的依赖(因此对运动轨迹的依赖)仍然是一个悬而未决的问题。本论文通过使用行走人类的三维重建来解决对轨迹的依赖问题。使用3D模型有几个值得一提的优点。首先,与仅从2D图像中提取描述符的方法相比,通过使用3D重建可以利用更多的信息。其次,3D重建可以沿着路线排列,就像受试者在跑步机上行走一样,从而提供了一种无论路径如何都能识别人的方法。为了解决对轨迹的依赖,提出了三种方法:(1)使用行走的人的对齐三维重建;(2)使用行走的人的未对齐三维重建。(3)在不使用三维重建的情况下提取三维描述。提出了三种步态描述符。第一个重点是通过三维对齐体的形态分析来描述步态。第二种是利用熵的概念来描述人的步态动力学。第三个目标是以旋转不变的方式捕获步态动力学,这使得识别在直线和曲线路径上行走的人变得有趣,无论方向如何变化。这些方法已经在“AVA多视图数据集(AVAMVG)”和“九州大学4D步态数据库(KY4D)”上进行了测试。这两个数据库都是专门设计来解决依赖于视点的问题,因此也就是依赖于轨迹的问题。实验结果表明,在基于对齐体重构的步态描述符方法中,基于熵的步态描述符与现有方法相比效果最好。然而,旋转不变步态描述符在不需要对三维步态重建进行对齐的情况下,实现了克服比较先进方法的识别率。
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来源期刊
Electronic Letters on Computer Vision and Image Analysis
Electronic Letters on Computer Vision and Image Analysis Computer Science-Computer Vision and Pattern Recognition
CiteScore
2.50
自引率
0.00%
发文量
19
审稿时长
12 weeks
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