The authors present results of the research on human recognition based on the video gait sequences from the CASIA Gait Database. Both linear (principal component analysis; PCA) and non-linear (isometric features mapping; Isomap and locally linear embedding; LLE) methods were applied in order to reduce data dimensionality, whereas a concept of hidden Markov model (HMM) was used for the purpose of data classification. The results of the conducted experiments formed the main subject of analysis of classification accuracy expressed by means of the Correct Classification Rate (CCR).


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Feature Extraction and HMM-Based Classification of Gait Video Sequences for the Purpose of Human Identification


    Weitere Titelangaben:

    Studies Comp.Intelligence


    Beteiligte:
    Nawrat, Aleksander (Herausgeber:in) / Kuś, Zygmunt (Herausgeber:in) / Josiński, Henryk (Autor:in) / Kostrzewa, Daniel (Autor:in) / Michalczuk, Agnieszka (Autor:in) / Świtoński, Adam (Autor:in) / Wojciechowski, Konrad (Autor:in)


    Erscheinungsdatum :

    01.01.2013


    Format / Umfang :

    13 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Gender Classification Based on Fusion of Multi-view Gait Sequences

    Huang, Guochang / Wang, Yunhong | Springer Verlag | 2007



    Statistical Feature Fusion for Gait-Based Human Recognition

    Han, J. / Bhanu, B. / IEEE Computer Society | British Library Conference Proceedings | 2004


    Gait recognition based on Gabor wavelets and modified gait energy image for human identification

    Huang, D.-Y. / Lin, T.-W. / Hu, W.-C. et al. | British Library Online Contents | 2013