National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
IEEE Trans Image Process. 2003;12(9):1120-31. doi: 10.1109/TIP.2003.815251.
Gait recognition has recently gained significant attention from computer vision researchers. This interest is strongly motivated by the need for automated person identification systems at a distance in visual surveillance and monitoring applications. The paper proposes a simple and efficient automatic gait recognition algorithm using statistical shape analysis. For each image sequence, an improved background subtraction procedure is used to extract moving silhouettes of a walking figure from the background. Temporal changes of the detected silhouettes are then represented as an associated sequence of complex vector configurations in a common coordinate frame, and are further analyzed using the Procrustes shape analysis method to obtain mean shape as gait signature. Supervised pattern classification techniques, based on the full Procrustes distance measure, are adopted for recognition. This method does not directly analyze the dynamics of gait, but implicitly uses the action of walking to capture the structural characteristics of gait, especially the shape cues of body biometrics. The algorithm is tested on a database consisting of 240 sequences from 20 different subjects walking at 3 viewing angles in an outdoor environment. Experimental results are included to demonstrate the encouraging performance of the proposed algorithm.
步态识别最近引起了计算机视觉研究人员的极大关注。这种兴趣的产生主要是由于在视觉监控和监测应用中需要远距离的自动化人员识别系统。本文提出了一种简单而有效的基于统计形状分析的自动步态识别算法。对于每一个图像序列，使用改进的背景减除程序从背景中提取行走人物的运动轮廓。然后，将检测到的轮廓的时间变化表示为在公共坐标框架中关联的复杂向量配置序列，并进一步使用普罗克鲁斯形状分析方法进行分析，以获得步态特征的平均形状。采用基于完整普罗克鲁斯距离度量的监督模式分类技术进行识别。该方法并不直接分析步态的动力学，而是通过行走的动作来捕获步态的结构特征，特别是身体生物特征的形状线索。该算法在一个由 20 个不同的人在户外环境中以 3 个视角行走的 240 个序列组成的数据库上进行了测试。实验结果表明了所提出算法的良好性能。
