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An Empirical Study for Human Behavior Analysis

机译:人类行为分析的实证研究

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This paper presents an empirical study for human behavior analysis based on three distinct feature extraction techniques: Histograms of Oriented Gradients (HOG), Local Binary Pattern (LBP) and Scale Invariant Local Ternary Pattern (SILTP). The utilised public videos representing spatio-temporal problem area of investigation include INRIA person detection and Weizmann pedestrian activity datasets. For INRIA dataset, both LBP and HOG were able to eliminate redundant video data and show human-intelligible feature visualisation of extracted features required for classification tasks. However, for Weizmann dataset only HOG feature extraction was found to work well with classifying five selected activities/exercises (walking, running, skipping, jumping and jacking).
机译:本文提出了一种基于三种不同特征提取技术的人类行为分析的实证研究:定向梯度直方图(HOG),局部二元模式(LBP)和尺度不变局部三元模式(SILTP)。代表调查时空问题领域的已利用公共视频包括INRIA人检测和Weizmann行人活动数据集。对于INRIA数据集,LBP和HOG都能够消除冗余视频数据,并显示分类任务所需的提取特征的人类可理解特征可视化。但是,对于Weizmann数据集,仅HOG特征提取可用于对五个选定的活动/锻炼(步行,跑步,跳跃,跳跃,跳跃和顶起)进行分类。

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