题名

Gait Analysis for Walking Paths Determination and Human Identification

并列篇名

步伐姿態分析運用於人物步行路徑與身分識別

DOI

10.29548/BGYY.201109.0001

作者

何孟芬(Meng-Fen Ho)；黃仲陵(Chung-Lin Huang)

关键词

步伐姿態分析 ； 人物辨識 ； Gait analysis ； human identification

期刊名称

修平學報

卷期/出版年月

23期（2011 / 09 / 01）

页次

1 - 16

内容语文

英文
中文摘要

在這篇論文中，我們擷取動態與靜態資訊來進行人物步行路徑和身分的識別。首先利用步伐擺幅距離的週期特性，可以估測每段影像的步伐週期，針對每段步伐週期，分析其運動向量的分布，即可獲得所需的動態資訊，同時利用傅立葉描述取得靜態資訊，接著將這兩種資訊轉換至低維度空間以便進行人物識別。為了解決訓練影片與測試影片中，人物走路速度可能不同的問題，我們提出了一個混合人物識別演算法來選定最有效的特徵。每當測試特徵向量進來時，利用最鄰近分類法則進行路徑確認與人物識別。本系統利用CASIA步伐姿態資料庫進行評估，實驗結果證明的確獲得極高的辨識率，以正常走路的資料組而言，可達到約98%。
英文摘要

In this paper, we propose a gait analysis method to extract the dynamic and static information from the input video for walking path determination and human identification. Based on the periodicity of swing distances, we may estimate the gait period of each walking video sequence. For each gait cycle, we depict the dynamic information by analyzing the distribution of motion vectors, and then describe the static information by using Fourier descriptors. The extracted dynamic and static information is transformed into lower dimensional embedding space for human identity recognition. To solve the difference of walking velocity between the test and training human objects, a hybrid human ID recognition algorithm is developed to choose the effective feature. Given a test feature vector, the nearest neighbor classifier is applied for walking paths determination and human identification. The proposed algorithm is evaluated on the CASIA gait database, and the experimental results demonstrate a highly acceptable recognition rate, for example, 98% for normal walking dataset.
主题分类 人文學 > 人文學綜合
基礎與應用科學 > 基礎與應用科學綜合
工程學 > 工程學綜合
工程學 > 機械工程
社會科學 > 社會科學綜合
参考文献
  1. CASIA Gait Database, http://www.sinobiometrics.com, 2006
  2. Bashir, K.,Xiang, T.,Gong, S.,Mary, Q.(2008).Feature Selection on Gait Energy Image for Human Identification.ICASSP 2008
  3. Bissacco, A.,Saisan, P.,Soatto, S.(2004).Gait Recognition using Dynamic Affine Invariants.Proc. of the MTNS
  4. Boulgouris, N. V.,Chi, Z. X.(2007).Gait Recognition Based on Human Body Components.ICIP 2007
  5. Boulgouris, N. V.,Chi, Z. X.(2007).Gait Recognition Using Random Transform and Linear Discriminant Analysis.IEEE Transactions on Image Processing,16(3),731-740.
  6. Boulgouris, N.V.,Hatzinakos, D.,Plataniotis, K.N.(2005).Gait Recognition: A Challenging Signal Processing Technology for Biometric Identification.IEEE Signal Processing Magazine,22(6),78-90.
  7. Chen, S.,Gao, Y.(2007).An Invariant Appearance Model for Gait Recognition.ICME 2007
  8. Cheng, M. H.,Ho, M. F.,Huang, C. L.(2008).Gait Analysis for Human Identification Through Manifold Learning and HMM.Pattern Recognition,41(8),2541-2553.
  9. Gonzales, R. C.,Woods, R. E..Digital Image Processing.Prentice Hall.
  10. Han, J.,Bhanu, B.(2006).Individual Recognition using Gait Energy Image.IEEE Transactions on Pattern Analysis and Machine Intelligence,28(2),316-322.
  11. Huang, P. S.,Harris, C. J.,Nixon, M. S.(1999).Recognizing Humans by Gait via Parametric Canonical Space.Artificial Intelligence in Eng.,13,359-366.
  12. Huang, X.,Boulgouris, N.(2009).Model-Based Human Gait Recognition Using Fusion of Features.ICASSP 2009
  13. Kale, A.,Chowdhury, R.,Chellappa, R.(2003).Towards a View Invariant Gait Recognition Algorithm.Proc. Of the IEEE Conf. on Advanced Video and Signal Based Surveillance
  14. Liu, J.,Zheng, N.(2007).Gait History Image: A Novel Temporal for Gait Recognition.ICME 2007
  15. Ma, Q.,Wang, S.,Nie, D.,Qiu, J.(2007).Recognizing Humans Based on Gait Moment Image.Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing 2007 IEEE
  16. Sarkar, S.,Phillips, P. J.,Liu, Z.,Vega, I. R.,Grother, P.,Bowyer, K. W.(2005).The Human ID Gait Challenge Problem: Data Sets, Performance, and Analysis.IEEE Transactions on Pattern Analysis and Machine Intelligence,27(2),162-177.
  17. Su, H.,Huang, F. G.(2005).Human Gait Recognition Based on Motion Analysis.Proceedings of the Fourth International Conference on Machine Learning and Cybernetics,Guangzhou:
  18. Yu, S.,Tan, D.,Tan, T.(2006).A Framework for Evaluating the Effect of View Angle, Clothing and Carrying Condition on Gait Recognition.ICPR 2006
print cancel