Probabilistic Model-Based Silhouette Refinement for Gait Recognition  

作　　者：张元元 吴晓娟 阮秋琦 

机构地区：[1]Institute of Image Processing and Pattern Recognition,Shandong University [2]Institute of Information Science,Beijing Jiaotong University

出　　处：《Journal of Shanghai Jiaotong university(Science)》2010年第1期24-30,共7页上海交通大学学报（英文版）

基　　金：the National Natural Science Foundation of China (No. 60675024)

摘　　要：An algorithm to refine and clean gait silhouette noises generated by imperfect motion detection techniques is developed,and a relatively complete and high quality silhouette is obtained.The silhouettes are sequentially refined in two levels according to two different probabilistic models.The first level is within-sequence refinement.Each silhouette in a particular sequence is refined by an individual model trained by the gait images from current sequence.The second level is between-sequence refinement.All the silhouettes that need further refinement are modified by a population model trained by the gait images chosen from a certain amount of pedestrians.The intention is to preserve the within-class similarity and to decrease the interaction between one class and others.Comparative experimental results indicate that the proposed algorithm is simple and quite effective,and it helps the existing recognition methods achieve a higher recognition performance.An algorithm to refine and clean gait silhouette noises generated by imperfect motion detection techniques is developed, and a relatively complete and high quality silhouette is obtained. The silhouettes are sequentially refined in two levels according to two different probabilistic models. The first level is within-sequence refinement. Each silhouette in a particular sequence is refined by an individual model trained by the gait images from current sequence. The second level is between-sequence refinement. All the silhouettes that need further refinement are modified by a population model trained by the gait images chosen from a certain amount of pedestrians. The intention is to preserve the within-class similarity and to decrease the interaction between one class and others. Comparative experimental results indicate that the proposed algorithm is simple and quite effective, and it helps the existing recognition methods achieve a higher recognition performance.

关 键 词：silhouette refinement probabilistic model gait recognition performance evaluation 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TP391.41[自动化与计算机技术—计算机科学与技术]