面向大规模社交活动或会议聚会的大型自动化参会者识别系统
Automated Attendee Recognition System for Large-Scale Social Events or Conference Gathering
AI总结：
针对大规模活动人工考勤效率低且易错的问题，提出基于云的自动化系统，利用出入口摄像头进行实时人脸检测与识别，在动态条件下实现90%总体准确率及无遮挡时100%准确率。
摘要：
在婚礼或会议等大规模活动中，人工考勤追踪通常效率低下且容易出现人为错误。为了解决这一挑战，我们提出了一种基于云的自动化考勤追踪系统，该系统使用安装在入口和出口闸门的摄像头。安装的摄像头持续捕获视频，并将视频数据发送到云服务以执行实时人脸检测和识别。与现有解决方案不同，我们的系统即使在参会者没有直视摄像头时也能准确识别他们，允许自然的动作，例如四处张望或边走边交谈。据我们所知，这是第一个在此类动态条件下实现高识别率的系统。我们的系统展示了总体90%的准确率，每个视频帧在5秒内处理完毕，确保实时运行而不丢帧。此外，通知会在相同的延迟内及时发送给安保人员。该系统对于没有面部遮挡的个人实现了100%的准确率，并成功识别了出现在摄像头视野内的所有参会者，为大规模社交活动中的参会者识别提供了稳健的解决方案。
英文摘要：
Manual attendance tracking at large-scale events, such as marriage functions or conferences, is often inefficient and prone to human error. To address this challenge, we propose an automated, cloud-based attendance tracking system that uses cameras mounted at the entrance and exit gates. The mounted cameras continuously capture video and send the video data to cloud services to perform real-time face detection and recognition. Unlike existing solutions, our system accurately identifies attendees even when they are not looking directly at the camera, allowing natural movements, such as looking around or talking while walking. To the best of our knowledge, this is the first system to achieve high recognition rates under such dynamic conditions. Our system demonstrates overall 90% accuracy, with each video frame processed in 5 seconds, ensuring real time operation without frame loss. In addition, notifications are sent promptly to security personnel within the same latency. This system achieves 100% accuracy for individuals without facial obstructions and successfully recognizes all attendees appearing within the camera's field of view, providing a robust solution for attendee recognition in large-scale social events.
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。
