Gait Recognition with Wavelet Moments of Silhouette Change Images

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Gait Recognition with Wavelet Moments of Silhouette Change Images

更新时间：2025-05-08

- Gait Recognition with Wavelet Moments of Silhouette Change Images
- Vol. 43, Issue 1, Pages: 90-94(2009)
- 作者机构：
  
  1. 西安电子科技大学计算机外部设备研究所,西安,710071
  2. 浙江万里学院设计学院,浙江,宁波,315100
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391.41
- Online First：10 January 2009，
  
  Published：2009
- 稿件说明：
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Gait Recognition with Wavelet Moments of Silhouette Change Images[J]. 2009, 43(1): 90-94.
DOI：

Gait Recognition with Wavelet Moments of Silhouette Change Images[J]. 2009, 43(1): 90-94. DOI：

摘要

针对计算机视觉中的步态图像提取问题

提出了一种基于行人轮廓变化的人体步态识别方法.对图像序列进行预处理

提取并采样行人轮廓

通过分析基于区域直方图的运动信号来估计2个单步长度

叠加前后帧间的新增轮廓区域和消失轮廓区域

从而构造出2组运动历史图像

并用其表达行人的步态特征

最后采用小波矩不变量提取这2组图像的特征

以作分类和识别之用.经Soton数据库实验表明

所提算法能很好地体现步态的时变信息和空间信息

大大降低了计算维数

所用小波矩的特征向量不仅具有平移、缩放和旋转不变性

而且具有局部性和多分辨率特征

正确识别率可达88.20%.

Abstract

A gait recognition method based on human silhouette changes for gait feature representation and extraction is proposed. The image sequence is preprocessed to extract and sample the binary human silhouette images

and two stride lengths are then estimated by analyzing a motion signal based on region histogram. By accumulating the appearance and disappearance of silhouettes between the neighbor frames

two sets of motion history images are constructed to represent the gait features. The wavelet moment invariants are employed to extract the features of these images for classification and recognition. Experiments on Soton gait database show that these algorithms enable to sufficiently describe temporal and spatial information

and largely reduce computation dimension; the feature vectors with wavelet moments are invariant to translation

scale change and rotation to provide localized and multi-resolution capacity with a correct classification rate of 88.20%.

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references

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