@inproceedings{60cc250fc3084d44b0ac6032c83d0486,
title = "Partitioning gait cycles adaptive to fluctuating periods and bad silhouettes",
abstract = "Period detection and cycle partitioning are always the very beginning for most gait recognition algorithms. Badly segmented silhouettes and random fluctuations in walking speed are two of the main problems for this basic but important issue. In this paper, we propose a method of cycle partitioning that is adaptive to silhouette quality and speed fluctuations. To do that, autocorrelation on sliding window is proposed to quantify the silhouette quality into {"}trusted zones{"} and {"}uncertain zones{"}. Prior period estimation and observation of fluctuations are incorporated to obtain more precise cycle detection. One criterion based on the difference of Common Phase Frames (CPF) is proposed to evaluate the precision of detection. In experiment, our method was compared with the traditional autocorrelation method using sequences from the USF gait database. The results showed the improved cycle partitioning performance of the proposed method.",
author = "Jianyi Liu and Nanning Zheng",
year = "2007",
doi = "10.1007/978-3-540-74549-5\_37",
language = "英语",
isbn = "9783540745488",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "347--355",
booktitle = "Advances in Biometrics - International Conference, ICB 2007, Proceedings",
note = "2007 International Conference on Advances in Biometrics, ICB 2007 ; Conference date: 27-08-2007 Through 29-08-2007",
}