CASIA OpenIR  > 智能感知与计算研究中心
How Scene Implies Action in Realistic Videos
Wang Hongsong(王洪松)1,2,3; Wang Wei(王威)1,2,3; Wang Liang(王亮)1,2,3
Conference NameInternational Conference on Image Processing (ICIP)
Conference Date2016
Conference PlacePhoenix, USA
AbstractPeople drive on the road and eat in the kitchen. Can the road imply driving or the kitchen imply eating? This paper addresses such a problem by studying the relations between actions and scenes. To get effective scene representation, we use a deep convolutional neural networks (CNN) model trained from a scene-centric database to predict scene responses for videos. We employ two encoding schemes based on frame features to represent the scene and its changes, respectively. We conduct experiments on two challenging datasets, HMDB51 and Hollywood2, and compare action recognition results of different encodings based on different scene features. Our results demonstrate that scene features, when combined with motion features, improve the state-of-the-art results for action recognition. Finally, we explore the relationship between actions and scenes by analyzing scene preferences to a particular action qualitatively and quantitatively.
Document Type会议论文
Affiliation1.Center for Research on Intelligent Perception and Computing
2.National Laboratory of Pattern Recognition
3.Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang Hongsong,Wang Wei,Wang Liang. How Scene Implies Action in Realistic Videos[C],2016:1619-1623.
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