View-invariant action recognition in surveillance videos

Fang Zhang, Yunhong Wang, Zhaoxiang Zhang
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引用次数: 13

Abstract

Recently, human action recognition has been a popular and important topic in computer vision. However, except some conventional problems such as noise, low resolution etc., view-invariant recognition is one of the most challenging problems. In this paper, we focus on solve multi-view action recognition from surveillance video. To detect moving objects from complicated backgrounds, this paper employs improved Gaussian mixed model, which uses K-means clustering to initialize the model and it gets better motion detection results for surveillance videos. We demonstrate the silhouette representation “Envelope Shape” can solve the viewpoint problem in surveillance videos. The experiment results demonstrate that our human action recognition system is fast and efficient on CASIA activity analysis database.
监控视频中的视不变动作识别
近年来,人体动作识别已成为计算机视觉领域的一个热点和重要课题。然而,除了一些传统的问题,如噪声、低分辨率等,视点不变识别是最具挑战性的问题之一。本文主要解决监控视频中的多视点动作识别问题。为了检测复杂背景下的运动物体,本文采用改进的高斯混合模型,该模型使用K-means聚类对模型进行初始化,得到了较好的监控视频运动检测结果。我们证明了轮廓表示“包络形状”可以解决监控视频中的视点问题。实验结果表明,我们的人体动作识别系统在CASIA活动分析数据库上是快速有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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