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Jointly Learning Multiple Sequential Dynamics for Human Action Recognition.

Liu AA, Su YT, Nie WZ, Yang ZX - PLoS ONE (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China.

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 propose the model learning and inference methods to discover temporal context within individual action unit sequence and the latent correlation among different body parts. Extensive experiments are implemented to demonstrate the superiority of the proposed method on two popular RGB human action datasets, KTH & TJU, and the depth dataset in MSR Daily Activity 3D.

No MeSH data available.


Related in: MedlinePlus

The confusion matrixes of All-MTCRFs on KTH, TJU, and MDA.
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pone.0130884.g007: The confusion matrixes of All-MTCRFs on KTH, TJU, and MDA.

Mentions: The comparison results are shown in Table 1. The confusion matrixes of the optimal performance on three datasets by All-MTCRFs are respectively shown in Fig 7a, 7b and 7c.


Jointly Learning Multiple Sequential Dynamics for Human Action Recognition.

Liu AA, Su YT, Nie WZ, Yang ZX - PLoS ONE (2015)

The confusion matrixes of All-MTCRFs on KTH, TJU, and MDA.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493153&req=5

pone.0130884.g007: The confusion matrixes of All-MTCRFs on KTH, TJU, and MDA.
Mentions: The comparison results are shown in Table 1. The confusion matrixes of the optimal performance on three datasets by All-MTCRFs are respectively shown in Fig 7a, 7b and 7c.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China.

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 propose the model learning and inference methods to discover temporal context within individual action unit sequence and the latent correlation among different body parts. Extensive experiments are implemented to demonstrate the superiority of the proposed method on two popular RGB human action datasets, KTH & TJU, and the depth dataset in MSR Daily Activity 3D.

No MeSH data available.


Related in: MedlinePlus