Feature extraction for human action classification using adaptive key frame interval

Kanokphan Lertniphonphan, S. Aramvith, T. Chalidabhongse
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引用次数: 4

Abstract

Human actions in video have the variation in both spatial and time domains which cause the difficulty for action classification. According to the nature of articulated body, an amount of movement from point-to-point is not constant, which can be illustrated as a bell-shape. In this paper, key frames are detected for specifying a starting and ending point for an action cycle. The time between key frames determines the window length for feature extraction in time domain. Since the cycles are varying, the key frame interval is varying and adaptive to performer and action. A local orientation histogram of Key Pose Energy Image (KPEI) and Motion History Image (MHI) is constructed during the period. The experimental results on WEIZMANN dataset demonstrate that the feature within the adaptive key frame interval can effectively classify actions.
基于自适应关键帧间隔的人体动作分类特征提取
视频中的人的动作在空间和时间上都有变化,这给动作分类带来了困难。根据铰接体的性质,从点到点的运动量不是恒定的,可以用钟形来表示。在本文中,检测关键帧用于指定动作循环的起点和终点。关键帧之间的时间决定了时域特征提取的窗口长度。由于周期是变化的,关键帧间隔是变化的,并适应表演者和动作。在此期间,构建了关键姿态能量图像(KPEI)和运动历史图像(MHI)的局部方向直方图。在WEIZMANN数据集上的实验结果表明,自适应关键帧间隔内的特征可以有效地对动作进行分类。
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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