Jointly Learning Multiple Sequential Dynamics for Human Action Recognition.

ABSTRACT: Discovering visual dynamics during human actions is a challenging task for human action recognition. To deal with this problem, we theoretically propose the multi-task conditional random fields model and explore its application on human action recognition. For visual representation, we propose the part-induced spatiotemporal action unit sequence to represent each action sample with multiple partwise sequential feature subspaces. For model learning, we propose the multi-task conditional random fields (MTCRFs) model to discover the sequence-specific structure and the sequence-shared relationship. Specifically, the multi-chain graph structure and the corresponding probabilistic model are designed to represent the interaction among multiple part-induced action unit sequences. Moreover we propo

SUBMITTER: Liu AA 

PROVIDER: S-EPMC4493153 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature