Expectiations and challenges for the Automatic Ecognition Technology. Kanako Takeguchi, NHK
H UMAN A CTION R ECOGNITION USING L OCAL S PATIO -T EMPORAL D ISCRIMINANT E MBEDDING Kui Jia and...
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Transcript of H UMAN A CTION R ECOGNITION USING L OCAL S PATIO -T EMPORAL D ISCRIMINANT E MBEDDING Kui Jia and...
HUMAN ACTION RECOGNITION USING LOCAL SPATIO-TEMPORAL DISCRIMINANTEMBEDDINGKui Jia and Dit-Yan Yeung, IEEE Conference on Computer Vision and Pattern Recognition
Instructor: Jenn-Jier LienReporter: Mei-Hsuan Chao
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OUTLINE
Introduction Related work Local spatio-temporal discriminant embedding Experiments Conclusion
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INTRODUCTION
Recognizing human activities in videos has many important computer vision applications.
A human silhouette contains both instant spatial information about the body pose and dynamic temporal motion information of the global body and local body parts.
Human silhouettes can be considered as data points on nonlinear dynamic shape manifolds.
The aim in this paper is to find a manifold embedding method which can optimally make use of the discriminative temporal shape variation information between different types of actions.
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RELATED WORK
LPP(Locality preserving projections) LPP constructs a nearest neighbor graph. By using the Laplacian of the graph, LPP can find a mapping
which optimally preserves the local neighborhood information.
LSDA(locality sensitive discriminant analysis) LSDA first constructs one nearest neighbor graph, and then
splits it into the within-class graph and the between-class graph.
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LOCAL SPATIO-TEMPORAL DISCRIMINANT EMBEDDING
Neighbor graph G
Short video segment Si
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LOCAL SPATIO-TEMPORAL DISCRIMINANT EMBEDDING
Objective functions
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LOCAL SPATIO-TEMPORAL DISCRIMINANT EMBEDDING
Principal angles between Si and Sj
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LOCAL SPATIO-TEMPORAL DISCRIMINANT EMBEDDING
Optimal embedding
The columns of an optimal A can be obtained as the generalized eigenvectors corresponding to the l largest eigenvalues.
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LOCAL SPATIO-TEMPORAL DISCRIMINANT EMBEDDING
Iterative learning
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EXPERIMENTS
Data setting
Design of two-stage recognition scheme
Frame by frame basis
Short segment
basis
Test silhouette
frame
Recognitionresult
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EXPERIMENTS
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CONCLUSION
Propose a novel local spatio-temporal discriminant embedding (LSTDE) method.
Perform recognition on a frame-by-frame or short video segment basis.
Experimental results demonstrate that the proposed method can accurately recognize human actions, and outperforms some representative manifold embedding methods.