Environment-independent In-baggage Object Identification Using WiFi Signals
Environment-independent In-baggage Object Identification Using WiFi Signals
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DOI：
10.1109/mass52906.2021.00018
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Cong Shi;Tianming Zhao;Yucheng Xie;Tianfang Zhang;Yan Wang;Xiaonan Guo;Yingying Chen
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文献类型：
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作者：
Cong Shi;Tianming Zhao;Yucheng Xie;Tianfang Zhang;Yan Wang;Xiaonan Guo;Yingying Chen
低成本的行李内物识别是提高公共安全和智能制造的迫切需求。现有的方法通常需要专门的设备和沉重的部署开销，这使得它们很难进行大规模部署。最近基于wifi的方法不适合实际部署，因为它没有解决动态环境影响。在这项工作中，我们提出了一种利用低成本WiFi的环境无关的行李内物体识别系统。我们利用通道状态信息（CSI）来捕获材料和形状特征，以方便细粒度的行李内物体识别。构建这样一个系统的一个主要挑战是CSI测量对现实世界的动态非常敏感，例如不同类型的行李、时变的环境噪声和干扰，以及不同的部署环境。为了解决这些问题，我们开发了基于极化定向天线的WiFi功能，可以捕捉物体的材料和形状特征。开发了一种基于卷积神经网络的模型，以建设性地整合WiFi功能并进行准确的行李内物体识别。我们还开发了一种基于材料的领域适应，使用对抗性学习来促进在不同环境中的快速部署。我们进行了广泛的实验，涉及14个表征对象，4种类型的袋子在3个不同的房间环境中。结果表明，我们的系统在相同环境下的目标识别准确率达到97%以上，当系统部署在新的环境中，我们的领域自适应方法可以将系统的目标识别准确率提高42%。
Low-cost in-baggage object identification is highly demanded in enhancing public safety and smart manufacturing. Existing approaches usually require specialized equipment and heavy deployment overhead, making them hard to scale for wide deployment. The recent WiFi-based approach is unsuitable for practical deployment as it did not address dynamic environmental impacts. In this work, we propose an environment-independent in-baggage object identification system by leveraging low-cost WiFi. We exploit the channel state information (CSI) to capture material and shape characteristics to facilitate fine-grained inbaggage object identification. A major challenge of building such a system is that CSI measurements are sensitive to real-world dynamics, such as different types of baggage, time-varying ambient noises and interferences, and different deployment environments. To tackle these problems, we develop WiFi features based on polarized directional antennas that can capture objects’ material and shape characteristics. A convolutional neural network-based model is developed to constructively integrate the WiFi features and perform accurate in-baggage object identification. We also develop a material-based domain adaptation using adversarial learning to facilitate fast deployments in different environments. We conduct extensive experiments involving 14 representation objects, 4 types of bags in 3 different room environments. The results show that our system can achieve over 97% in the same environment, and our domain adaptation method can improve the object identification accuracy by 42% when the system is deployed in a new environment with little training.