Abstract
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.
Abstract (translated)
在大型活动（如婚礼或会议）中，手动签到通常效率低下且容易出错。为了应对这一挑战，我们提出了一种自动化、基于云的签到跟踪系统，该系统使用安装在入口和出口处的摄像头。这些摄像头会持续捕捉视频并将数据发送至云端进行实时面部检测和识别。与现有的解决方案不同，我们的系统即使当参与者不直接面向摄像头时也能够准确地识别他们，允许他们在自然动作中被识别，例如四处张望或边走路边交谈。 据我们所知，这是首个在如此动态条件下仍能实现高识别率的系统。该系统的整体准确性达到90%，每帧视频处理时间仅为5秒，确保了实时操作且无数据丢失。此外，安全人员会在相同的延迟时间内收到即时通知。对于没有面部遮挡的人来说，该系统的准确率为100%；并且能够成功识别所有出现在摄像头视野内的参与者，为大型社交活动中的参会者识别提供了一种可靠的解决方案。
URL
https://arxiv.org/abs/2503.03330
