Fast action detection via discriminative random forest voting and top-K subvolume search

Multiclass action detection in complex scenes is a challenging problem because of cluttered backgrounds and the large intra-class variations in each type of actions. To achieve efficient and robust action detection, we characterize a video as a collection of spatio-temporal interest points, and loca...

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Detalles Bibliográficos
Publicado:	2011
Materias:	Action detection branch and bound random forest top-K search Complex scenes Data sets Detection methods Interest points Intra-class variation Multi-class Mutual informations Orders of magnitude Random forests Search Algorithms Spatio-temporal Subvolumes Linear programming Decision trees
Acceso en línea:	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_15209210_v13_n3_p507_Yu http://hdl.handle.net/20.500.12110/paper_15209210_v13_n3_p507_Yu
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	paper:paper_15209210_v13_n3_p507_Yu
record_format	dspace
spelling	paper:paper_15209210_v13_n3_p507_Yu2025-07-30T18:54:57Z Fast action detection via discriminative random forest voting and top-K subvolume search Action detection branch and bound random forest top-K search Action detection branch and bound Complex scenes Data sets Detection methods Interest points Intra-class variation Multi-class Mutual informations Orders of magnitude random forest Random forests Search Algorithms Spatio-temporal Subvolumes top-K search Linear programming Decision trees Multiclass action detection in complex scenes is a challenging problem because of cluttered backgrounds and the large intra-class variations in each type of actions. To achieve efficient and robust action detection, we characterize a video as a collection of spatio-temporal interest points, and locate actions via finding spatio-temporal video subvolumes of the highest mutual information score towards each action class. A random forest is constructed to efficiently generate discriminative votes from individual interest points, and a fast top-K subvolume search algorithm is developed to find all action instances in a single round of search. Without significantly degrading the performance, such a top-K search can be performed on down-sampled score volumes for more efficient localization. Experiments on a challenging MSR Action Dataset II validate the effectiveness of our proposed multiclass action detection method. The detection speed is several orders of magnitude faster than existing methods. © 2011 IEEE. 2011 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_15209210_v13_n3_p507_Yu http://hdl.handle.net/20.500.12110/paper_15209210_v13_n3_p507_Yu
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	Action detection branch and bound random forest top-K search Action detection branch and bound Complex scenes Data sets Detection methods Interest points Intra-class variation Multi-class Mutual informations Orders of magnitude random forest Random forests Search Algorithms Spatio-temporal Subvolumes top-K search Linear programming Decision trees
spellingShingle	Action detection branch and bound random forest top-K search Action detection branch and bound Complex scenes Data sets Detection methods Interest points Intra-class variation Multi-class Mutual informations Orders of magnitude random forest Random forests Search Algorithms Spatio-temporal Subvolumes top-K search Linear programming Decision trees Fast action detection via discriminative random forest voting and top-K subvolume search
topic_facet	Action detection branch and bound random forest top-K search Action detection branch and bound Complex scenes Data sets Detection methods Interest points Intra-class variation Multi-class Mutual informations Orders of magnitude random forest Random forests Search Algorithms Spatio-temporal Subvolumes top-K search Linear programming Decision trees
description	Multiclass action detection in complex scenes is a challenging problem because of cluttered backgrounds and the large intra-class variations in each type of actions. To achieve efficient and robust action detection, we characterize a video as a collection of spatio-temporal interest points, and locate actions via finding spatio-temporal video subvolumes of the highest mutual information score towards each action class. A random forest is constructed to efficiently generate discriminative votes from individual interest points, and a fast top-K subvolume search algorithm is developed to find all action instances in a single round of search. Without significantly degrading the performance, such a top-K search can be performed on down-sampled score volumes for more efficient localization. Experiments on a challenging MSR Action Dataset II validate the effectiveness of our proposed multiclass action detection method. The detection speed is several orders of magnitude faster than existing methods. © 2011 IEEE.
title	Fast action detection via discriminative random forest voting and top-K subvolume search
title_short	Fast action detection via discriminative random forest voting and top-K subvolume search
title_full	Fast action detection via discriminative random forest voting and top-K subvolume search
title_fullStr	Fast action detection via discriminative random forest voting and top-K subvolume search
title_full_unstemmed	Fast action detection via discriminative random forest voting and top-K subvolume search
title_sort	fast action detection via discriminative random forest voting and top-k subvolume search
publishDate	2011
url	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_15209210_v13_n3_p507_Yu http://hdl.handle.net/20.500.12110/paper_15209210_v13_n3_p507_Yu
_version_	1840325327540191232

Fast action detection via discriminative random forest voting and top-K subvolume search

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