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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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_15209210_v13_n3_p507_Yu |
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todo:paper_15209210_v13_n3_p507_Yu2023-10-03T16:20:35Z Fast action detection via discriminative random forest voting and top-K subvolume search Yu, G. Goussies, N.A. Yuan, J. Liu, Z. 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. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar 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 |
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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 Yu, G. Goussies, N.A. Yuan, J. Liu, Z. 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. |
| format |
JOUR |
| author |
Yu, G. Goussies, N.A. Yuan, J. Liu, Z. |
| author_facet |
Yu, G. Goussies, N.A. Yuan, J. Liu, Z. |
| author_sort |
Yu, G. |
| 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 |
| url |
http://hdl.handle.net/20.500.12110/paper_15209210_v13_n3_p507_Yu |
| work_keys_str_mv |
AT yug fastactiondetectionviadiscriminativerandomforestvotingandtopksubvolumesearch AT goussiesna fastactiondetectionviadiscriminativerandomforestvotingandtopksubvolumesearch AT yuanj fastactiondetectionviadiscriminativerandomforestvotingandtopksubvolumesearch AT liuz fastactiondetectionviadiscriminativerandomforestvotingandtopksubvolumesearch |
| _version_ |
1807318624846741504 |