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Space-Time Flexible Kernel for Recognizing Activities from Wearable Cameras

机译:用于识别可穿戴式摄像机活动的时空灵活内核

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Recognizing activities of daily living is useful for ambient assisted living. In this regard, the use of wearable cameras is a promising technology. In this paper, we propose a novel approach for recognizing activities of daily living using egocentric viewpoint video clips. First, in every frame the appearing objects are detected and labelled depending if they are being used or not by the subject. Later, the video clip is divided into spatio temporal bins created with an object centric cut. Finally, a support vector machine classifier is computed using a spatio-temporal flexible kernel between video clips. The validity of the proposed method has been proved by conducting experiments in the ADL dataset. Results confirm the suitability of using the space-time location of objects as information for the classification of activities using an egocentric viewpoint.
机译:认识到日常生活活动对于环境辅助生活很有用。在这方面,可穿戴式相机的使用是有前途的技术。在本文中,我们提出了一种新颖的方法来使用以自我为中心的视点视频剪辑来识别日常生活的活动。首先,在每一帧中,根据对象是否正在使用来检测并标记出现的对象。随后,将视频剪辑分为以对象为中心的剪切创建的时空时区。最后,使用视频片段之间的时空灵活核来计算支持向量机分类器。通过在ADL数据集中进行实验,证明了该方法的有效性。结果证实了使用对象的时空位置作为信息以使用以自我为中心的观点对活动进行分类的适用性。

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