Context-Sensitive Human Activity Classification in Collaborative Learning Environments
Context-Sensitive Human Activity Classification in Collaborative Learning Environments
复制标题
协作学习环境中上下文相关的人类活动分类
DOI：
10.1109/ssiai.2018.8470331
复制
发表时间：
2018
期刊：
影响因子：
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通讯作者：
Carlos A. LópezLeiva
中科院分区：
文献类型：
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作者：
A. Jacoby;M. Pattichis;Sylvia Celedón;Carlos A. LópezLeiva
人类活动分类仍然具有挑战性，因为消除结构噪声的强烈需求、多种可能的活动以及视频采集中的强烈变化。本文探讨了协作学习环境中人类活动分类的研究，探讨了基于颜色的对象检测与对象交互的情境化相结合来分离特定于每个人类活动的运动矢量。基本方法是为每个活动使用单独的分类器。在这里，我们考虑的是对原始视频中打字、书写和说话活动的检测。使用43个未裁剪的视频片段来测试该方法，其中620个视频片段用于书写，1050个用于打字，1755个用于说话。使用简单的KNN分类器，该方法的书写准确率为72.6%，打字准确率为71%，说话准确率为84.6%。通过使用深度神经网络，分类准确率提高到92.5%(书写)、82.5%(打字)和99.7%(说话)。
Human activity classification remains challenging due to the strong need to eliminate structural noise, the multitude of possible activities, and the strong variations in video acquisition. The current paper explores the study of human activity classification in a collaborative learning environment.This paper explores the use of color based object detection in conjunction with contextualization of object interaction to isolate motion vectors specific to each human activity. The basic approach is to make use of separate classifiers for each activity. Here, we consider the detection of typing, writing, and talking activities in raw videos.The method was tested using 43 uncropped video clips with 620 video frames for writing, 1050 for typing, and 1755 frames for talking. Using simple KNN classifiers, the method gave accuracies of 72.6% for writing, 71% for typing and 84.6% for talking. Classification accuracy improved to 92.5% (writing), 82.5% (typing) and 99.7% (talking) with the use of Deep Neural Networks.