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

@inproceedings{lv06, 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.}, author = {Lv, Fengjun and Nevatia, Ramakant}, booktitle = {ECCV'06: Proceedings of the 9th European Conference on Computer Vision}, citeulike-article-id = {4755823}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/11744085_28}, citeulike-linkout-1 = {http://www.springerlink.com/content/ekg4662x44780232}, doi = {10.1007/11744085_28}, journal = {Computer Vision – ECCV 2006}, keywords = {action, hmm}, pages = {359--372}, posted-at = {2009-06-05 21:33:13}, priority = {2}, title = {Recognition and Segmentation of 3-D Human Action Using HMM and Multi-class AdaBoost}, url = {http://dx.doi.org/10.1007/11744085_28}, year = {2006} }

TY - CONF ID - lv06 L3 - citeulike-article-id:4755823 N2 - 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. JF - Computer Vision – ECCV 2006 EP - 372 TI - Recognition and Segmentation of 3-D Human Action Using HMM and Multi-class AdaBoost T2 - ECCV'06: Proceedings of the 9th European Conference on Computer Vision SP - 359 KW - action KW - hmm AU - Lv, Fengjun AU - Nevatia, Ramakant PY - 2006/// UR - http://dx.doi.org/10.1007/11744085_28 ER -