How Scene Implies Action in Realistic Videos

CASIA OpenIR > 模式识别实验室

	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
	2016
会议名称	International Conference on Image Processing (ICIP)
页码	1619-1623
会议日期	2016
会议地点	Phoenix, USA
摘要	People 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.
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/14058
专题	模式识别实验室
作者单位	1.Center for Research on Intelligent Perception and Computing 2.National Laboratory of Pattern Recognition 3.Institute of Automation, Chinese Academy of Sciences
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Wang Hongsong,Wang Wei,Wang Liang. How Scene Implies Action in Realistic Videos[C],2016:1619-1623.

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