Robust Post-processing Strategy for Gait Silhouette

摘要：

An integrated silhouette with perfect appearance is helpful for gait recognition. However, the silhouettes often have holes or missing parts, because the commonly used motion detection and extraction methods are not always suitable for every case. A robust post-processing strategy is proposed here to refine the raw silhouettes. First, an individual silhouette model which represents the mutual characteristic of a certain sequence is trained to update the current sequence. Then a population silhouette model which represents the mutual characteristic of all sequences in the dataset is trained to update the sequences that need further refinement. The experiments on NLPR database show that the proposed algorithm is quite effective and helps the existing recognition method to achieve higher classification performance.

关键词： gait recognition population model individual model silhouette refinement

作者: Yuanyuan Zhang Xiaojuan Wu Tingting Guo Xiuyuan Li Qiuqi Ruan

作者单位: School of Information Science & Engineering Shandong University Jinan, China Institute of Information Science Beijing Jiaotong University Beijing, China

会议类型: 国际会议

会议名称: 2009 2nd IEEE International Conference on Computer Science and Information Technology(第二届计算机科学与信息技术国际会议 ICCSIT2009)

会议地点: 北京

会议语种:英文

页码: 2424-2427

在线出版日期: 2009-08-08（万方平台首次上网日期，不代表论文的发表时间）