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Learning to See Through a Few Pixels: Multi Streams Network for Extreme Low-Resolution Action Recognition

机译:学习通过几个像素查看:多流网络,用于极端低分辨率动作识别

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Human action recognition is one of the most pressing questions in societal emergencies of any kind. Technology is helping to solve such problems at the cost of stealing human privacy. Several approaches have considered the relevance of privacy in the pervasive process of observing people. New algorithms have been proposed to deal with low-resolution images hiding people identity. However, many of these methods do not consider that social security asks for real-time solutions: active cameras require flexible distributed systems in sensible areas as airports, hospitals, stations, squares and roads. To conjugate both human privacy and real-time supervision, we propose a novel deep architecture, the Multi Streams Network . This model works in real-time and performs action recognition on extremely low-resolution videos, exploiting three sources of information: RGB images, optical flow and slack mask data. Experiments on two datasets show that our architecture improves the recognition accuracy compared to the two-streams approach and ensure real-time execution on Edge TPU (Tensor Processing Unit).
机译:人类行动认可是任何类型的社会紧急情况下最紧迫的问题之一。技术有助于解决窃取人类隐私的成本问题。几种方法认为隐私在观察人们的普遍过程中的相关性。已经提出了新的算法来处理隐藏人们身份的低分辨率图像。然而,许多这些方法都不认为社会保障要求实时解决方案:活动摄像机需要灵活的分布式系统,作为机场,医院,站,正方形和道路。为了共用人类隐私和实时监督,我们提出了一种新的深度建筑,<斜体XMLNS:MML =“http://www.w3.org/1998/math/mathml”xmlns:xlink =“http:/ /www.w3.org/1999/xlink"> multi流网络。该模型实时工作,并对极低分辨率的视频进行动作识别,利用三种信息来源:RGB图像,光流和松弛掩码数据。两个数据集的实验表明,与双流方法相比,我们的架构提高了识别准确性,并确保了边缘TPU(张量处理单元)上的实时执行。

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