Modeling the Temporal Extent of Actions

被引：0
|
作者：
Satkin, Scott [1 ]
Hebert, Martial [1 ]
机构：
[1] Carnegie Mellon Univ, Inst Robot, Pittsburgh, PA 15213 USA
来源：
COMPUTER VISION-ECCV 2010, PT I | 2010年 / 6311卷
关键词：
VIDEO;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we present a framework for estimating what portions of videos are most discriminative for the task of action recognition. We explore the impact of the temporal cropping of training videos on the overall accuracy of an action recognition system, and we formalize what makes a set of croppings optimal. In addition, we present; an algorithm to determine the best set of croppings for a dataset, and experimentally show that our approach increases the accuracy of various state-of-the-art action recognition techniques.
引用
收藏
页码：536 / 548
页数：13
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