Lost and Found!: associating target persons in camera surveillance footage with smartphone identifiers
Lost and Found!: associating target persons in camera surveillance footage with smartphone identifiers
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失物招领！：将摄像机监控录像中的目标人员与智能手机标识符相关联
DOI：
10.1145/3458864.3466904
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Lu, Hongsheng
中科院分区：
文献类型：
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作者：
Liu, Hansi;Alali, Abrar;Ibrahim, Mohamed;Li, Hongyu;Gruteser, Marco;Jain, Shubham;Dana, Kristin;Ashok, Ashwin;Cheng, Bin;Lu, Hongsheng
我们展示了一个应用程序，发现目标人物的监控视频。每个视觉检测到的参与者都被标记有智能手机ID，并且突出显示具有查询ID的目标人员。这项工作的动机是这样一个事实，即建立相机图像中观察到的主题和从他们的无线设备发送的消息之间的关联可以实现快速和可靠的标记。当需要在公共监控录像中找到目标行人时，这一点特别有用，而无需依赖面部识别。底层系统使用多模态方法，该方法利用WiFi精细定时测量（FTM）和惯性传感器（IMU）数据将每个视觉检测到的个体与相应的智能手机标识符相关联。这些智能手机测量结果与来自相机的RGB-D信息策略性地结合在一起，以使用多模态深度学习网络来学习亲和矩阵。
We demonstrate an application of finding target persons on a surveillance video. Each visually detected participant is tagged with a smartphone ID and the target person with the query ID is highlighted. This work is motivated by the fact that establishing associations between subjects observed in camera images and messages transmitted from their wireless devices can enable fast and reliable tagging. This is particularly helpful when target pedestrians need to be found on public surveillance footage, without the reliance on facial recognition. The underlying system uses a multi-modal approach that leverages WiFi Fine Timing Measurements (FTM) and inertial sensor (IMU) data to associate each visually detected individual with a corresponding smartphone identifier. These smartphone measurements are combined strategically with RGB-D information from the camera, to learn affinity matrices using a multi-modal deep learning network.