一种基于光流的多区域分割在步态识别中的应用

Application of Segmentation Based on Optical Flow in Gait Recognition

人体目标分割的质量对步态识别的性能有直接的影响。提出了一种鲁棒性的步态表示方法,即利用光流特征提取视频中的运动信息,并将目标人体区域部分按人体结构特点划分为多个子区域,每个子区域通过基于光流特征的椭圆模型进行拟合,建立多区域椭圆模型的人体结构模型。识别过程中将模型参数作为步态特征,结合动态时间规整技术解决了动态模式的相似度量和匹配问题。实验表明,该算法可以有效地提高识别算法的鲁棒性,并且具有较好的识别性能。

The quality of human silhouettes has a direct effect on gait recognition performance.This paper proposed a robust gait representation scheme to suppress the influence of silhouette incompleteness.By means of dividing human body area in a video sequence into several sub-areas and representing each sub-area by an ellipse whose parameters can be calculated from the corresponding motion information extracted from optical flow field,a new body structure model called multi-linked ellipse model was established.In the recognition stage,the parameters of model are finally used to achieve gait recognition based on dynamic time warping technology.Experimental results prove the higher performance of the method.