基于卷积神经网络热图的人脸跟踪

Nhu-Tai Do, Soohyung Kim, Hyung-Jeong Yang, Gueesang Lee, In Seop Na
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引用次数: 7

摘要

在本文中,我们将热图方法应用于人脸跟踪。我们利用卷积神经网络(CNN)提取的热图进行人脸/非人脸分类问题。我们构建的CNN架构是一个浅层网络,用于提取对定位目标有意义的信息。此外,我们制作了许多cnn,改变了最后一层池的大小,以获得一个定义良好的热图。在视觉跟踪目标数据集上的实验表明,该方法的效果是令人鼓舞的。这表明了我们提出的方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Face tracking with convolutional neural network heat-map
In this paper, we apply a heat-map approach for human face tracking. We utilize the heat-map extracted from the convolutional neural networks (CNN) for face / non-face classification problem. The CNN architecture we build is a shallow network to extract information that is meaningful in locating an object. In addition, we made many CNNs with changes in pool-size of the last layer to obtain a well-defined heat-map. Experiments in the Visual Tracking Object dataset show that the results of the method are very encouraging. This shows the effectiveness of our proposed method.
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