Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/171659
Title: Modeling 4D Human-Object Interactions for Joint Event Segmentation, Recognition, and Object Localization
Authors: Ping Wei;Yibiao Zhao;Nanning Zheng;Song-Chun Zhu
Year: 2017
Publisher: IEEE
Abstract: In this paper, we present a 4D human-object interaction (4DHOI) model for solving three vision tasks jointly: i) event segmentation from a video sequence, ii) event recognition and parsing, and iii) contextual object localization. The 4DHOI model represents the geometric, temporal, and semantic relations in daily events involving human-object interactions. In 3D space, the interactions of human poses and contextual objects are modeled by semantic co-occurrence and geometric compatibility. On the time axis, the interactions are represented as a sequence of atomic event transitions with coherent objects. The 4DHOI model is a hierarchical spatial-temporal graph representation which can be used for inferring scene functionality and object affordance. The graph structures and parameters are learned using an ordered expectation maximization algorithm which mines the spatial-temporal structures of events from RGB-D video samples. Given an input RGB-D video, the inference is performed by a dynamic programming beam search algorithm which simultaneously carries out event segmentation, recognition, and object localization. We collected a large multiview RGB-D event dataset which contains 3,815 video sequences and 383,036 RGB-D frames captured by three RGB-D cameras. The experimental results on three challenging datasets demonstrate the strength of the proposed method.
URI: http://localhost/handle/Hannan/171659
volume: 39
issue: 6
More Information: 1165,
1179
Appears in Collections:2017

Files in This Item:
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7482729.pdf2.53 MBAdobe PDF
Title: Modeling 4D Human-Object Interactions for Joint Event Segmentation, Recognition, and Object Localization
Authors: Ping Wei;Yibiao Zhao;Nanning Zheng;Song-Chun Zhu
Year: 2017
Publisher: IEEE
Abstract: In this paper, we present a 4D human-object interaction (4DHOI) model for solving three vision tasks jointly: i) event segmentation from a video sequence, ii) event recognition and parsing, and iii) contextual object localization. The 4DHOI model represents the geometric, temporal, and semantic relations in daily events involving human-object interactions. In 3D space, the interactions of human poses and contextual objects are modeled by semantic co-occurrence and geometric compatibility. On the time axis, the interactions are represented as a sequence of atomic event transitions with coherent objects. The 4DHOI model is a hierarchical spatial-temporal graph representation which can be used for inferring scene functionality and object affordance. The graph structures and parameters are learned using an ordered expectation maximization algorithm which mines the spatial-temporal structures of events from RGB-D video samples. Given an input RGB-D video, the inference is performed by a dynamic programming beam search algorithm which simultaneously carries out event segmentation, recognition, and object localization. We collected a large multiview RGB-D event dataset which contains 3,815 video sequences and 383,036 RGB-D frames captured by three RGB-D cameras. The experimental results on three challenging datasets demonstrate the strength of the proposed method.
URI: http://localhost/handle/Hannan/171659
volume: 39
issue: 6
More Information: 1165,
1179
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7482729.pdf2.53 MBAdobe PDF
Title: Modeling 4D Human-Object Interactions for Joint Event Segmentation, Recognition, and Object Localization
Authors: Ping Wei;Yibiao Zhao;Nanning Zheng;Song-Chun Zhu
Year: 2017
Publisher: IEEE
Abstract: In this paper, we present a 4D human-object interaction (4DHOI) model for solving three vision tasks jointly: i) event segmentation from a video sequence, ii) event recognition and parsing, and iii) contextual object localization. The 4DHOI model represents the geometric, temporal, and semantic relations in daily events involving human-object interactions. In 3D space, the interactions of human poses and contextual objects are modeled by semantic co-occurrence and geometric compatibility. On the time axis, the interactions are represented as a sequence of atomic event transitions with coherent objects. The 4DHOI model is a hierarchical spatial-temporal graph representation which can be used for inferring scene functionality and object affordance. The graph structures and parameters are learned using an ordered expectation maximization algorithm which mines the spatial-temporal structures of events from RGB-D video samples. Given an input RGB-D video, the inference is performed by a dynamic programming beam search algorithm which simultaneously carries out event segmentation, recognition, and object localization. We collected a large multiview RGB-D event dataset which contains 3,815 video sequences and 383,036 RGB-D frames captured by three RGB-D cameras. The experimental results on three challenging datasets demonstrate the strength of the proposed method.
URI: http://localhost/handle/Hannan/171659
volume: 39
issue: 6
More Information: 1165,
1179
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7482729.pdf2.53 MBAdobe PDF