视觉与无线定位的相遇：基于循环语境传播的有效人员再识别
Vision Meets Wireless Positioning: Effective Person Re-identification with Recurrent Context Propagation
摘要：
现有的人的重新识别方法依赖于视觉传感器来捕获行人。视觉传感器获取的图像或视频数据不可避免地会受到遮挡和行人姿态变化的影响，从而降低了行人的再识别性能，进一步限制了其在开放环境中的应用。另一方面，对于大多数人来说，最重要的随身物品之一就是手机，手机可以通过WiFi和蜂窝网络以无线定位信号的形式感知。这种信号对行人遮挡和视觉外观变化具有鲁棒性，但存在一定的定位误差。在这项工作中，我们的方法与视觉和无线定位的传感数据的人重新识别。为了利用这种跨模态的线索，我们提出了一种新的经常性的上下文传播模块，使信息之间的视觉数据和无线定位数据的传播，并最终提高匹配精度。为了评估我们的方法，我们贡献了一个新的无线定位人员重新识别（WP-ReID）数据集。大量的实验表明，该算法的有效性。代码将在https://github.com/yolomax/WP-ReID上发布。
英文摘要：
Existing person re-identification methods rely on the visual sensor to capture the pedestrians. The image or video data from visual sensor inevitably suffers the occlusion and dramatic variations of pedestrian postures, which degrades the re-identification performance and further limits its application to the open environment. On the other hand, for most people, one of the most important carry-on items is the mobile phone, which can be sensed by WiFi and cellular networks in the form of a wireless positioning signal. Such signal is robust to the pedestrian occlusion and visual appearance change, but suffers some positioning error. In this work, we approach person re-identification with the sensing data from both vision and wireless positioning. To take advantage of such cross-modality cues, we propose a novel recurrent context propagation module that enables information to propagate between visual data and wireless positioning data and finally improves the matching accuracy. To evaluate our approach, we contribute a new Wireless Positioning Person Re-identification (WP-ReID) dataset. Extensive experiments are conducted and demonstrate the effectiveness of the proposed algorithm. Code will be released at https://github.com/yolomax/WP-ReID.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
