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ECCV
2006
Springer

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

8 years 5 months ago
Recognition and Segmentation of 3-D Human Action Using HMM and Multi-class AdaBoost
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...
Fengjun Lv, Ramakant Nevatia
Added 13 Oct 2010
Updated 13 Oct 2010
Type Conference
Year 2006
Where ECCV
Authors Fengjun Lv, Ramakant Nevatia
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