Abstract Our goal is to automatically segment and recognize basic human actions, such as stand, walk and wave hands, from a sequence of joint positions or pose angles. Such recognition is difficult due to high dimensionality of the data and large spatial and temporal variations in the same action. We decompose the high dimensional 3-D joint space into a set of feature spaces where each feature corresponds to the motion of a single joint or combination of related multiple joints. For each feature, the dynamics of each action class is learned with one HMM. Given a sequence, the observation probability is computed in each HMM and a weak classifier for that feature is formed based on those probabilities. The weak classifiers with strong discriminative power are then combined by the Multi-Class AdaBoost (AdaBoost.M2) algorithm. A dynamic programming algorithm is applied to segment and recognize actions simultaneously. Results of recognizing 22 actions on a large number of motion capture sequences as well as several annotated and automatically tracked sequences show the effectiveness of the proposed algorithms.


    Access

    Download


    Export, share and cite



    Title :

    Recognition and Segmentation of 3-D Human Action Using HMM and Multi-class AdaBoost


    Contributors:


    Publication date :

    2006-01-01


    Size :

    14 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Recognition and Segmentation of 3-D Human Action Using HMM and Multi-class AdaBoost

    Lv, F. / Nevatia, R. | British Library Conference Proceedings | 2006



    Driver’s cognitive distraction detection using AdaBoost on pattern recognition basis

    Miyaji, Masahiro / Danno, Mikio / Kawanaka, Haruki et al. | IEEE | 2008


    Human Action Segmentation and Recognition Using Discriminative Semi-Markov Models

    Shi, Q. / Cheng, L. / Wang, L. et al. | British Library Online Contents | 2011


    An improved adaboost learning scheme using LDA features for object recognition

    Nunn, Christian / Kummert, Anton / Muller, Dennis et al. | IEEE | 2009