[1]王博,李燕.视频序列中的时空兴趣点检测及其自适应分析[J].计算机技术与发展,2014,24(04):49-52.
　WANG Bo,LI Yan.Space-time Interest Points Detection in Video Sequence and Its Adaptive Analysis[J].,2014,24(04):49-52.
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视频序列中的时空兴趣点检测及其自适应分析()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
24
期数:
2014年04期
页码:
49-52
栏目:
智能、算法、系统工程
出版日期:
2014-04-30

文章信息/Info

Title:
Space-time Interest Points Detection in Video Sequence and Its Adaptive Analysis
文章编号:
1673-629X（2014）04-0049-04
作者:
王博; 李燕
南京邮电大学
Author(s):
WANG Bo; LI Yan
关键词:
行为识别; Harris角点检测; 时空兴趣点; 自适应分析
Keywords:
action recognition; Harris corner detection; space-time interest points; adaptive analysis
分类号:
TP301
文献标志码:
A
摘要:
基于时空兴趣点的行为识别方法是目前较为流行的行为识别方法之一，它通过检测像素值在时空邻域有显著变化的兴趣点并从中提取底层特征来进行行为描述。由于时空兴趣点提取的是局部特征，不易受光照、运动特性、背景变化等影响，使这一方法具有较好的鲁棒性。文中采用尺度自适应的兴趣点检测算法，首先给出其详细的数学推理并对其自适应性进行分析，然后提取兴趣点局部邻域特征并用SVM方法对其进行分类操作以达到行为识别的目的。实验结果表明，该方法具有较好的尺度自适应性和识别结果。
Abstract:
Space-time interest points based method is one of the most prevalent approaches of action recognition in recent years, this method represents actions by low-level features extracted from space-time interest points which has a significant local variation of image intensities in spatio-temporal domain. Because the low-level features are local features actually hence suffer little influence of lighting,in-dividual patterns of motion,nonstationary backgrounds due to their local nature,this method is more robust than others. In this paper,em-ploy an adaptive interest points detection algorithm. First,a detailed mathematical proof of interest points detection and its adaptive analy-sis is given,and then,extract the local space-time features and combine it with SVM classification schemes for action recognition. Experi-ment proves that this method has a good adaptive nature and a good recognition results.

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更新日期/Last Update: 1900-01-01