Abstract
The advent of the Edge Computing (EC) leads to a huge ecosystem where numerous nodes can interact with data collection devices located close to end users. Human detection and tracking can be realized at edge nodes that perform the surveillance of an area under consideration through the assistance of a set of sensors (e.g., cameras). Our target is to incorporate the discussed functionalities to embedded devices present at the edge keeping their size limited while increasing their processing capabilities. In this paper, we propose two models for human detection accompanied by algorithms for tracing the corresponding trajectories. We provide the description of the proposed models and extend them to meet the challenges of the problem. Our evaluation aims at identifying models' accuracy while presenting their requirements to have them executed in embedded devices.
Abstract (translated)
边缘计算的出现导致了一个巨大的生态系统,其中许多节点可以与靠近最终用户的数据采集设备相互作用。人类检测和跟踪可以在边缘节点实现,通过一组传感器(例如摄像头)协助,对所考虑的区域进行监控。我们的的目标是将讨论的功能性添加到位于边缘的嵌入式设备中,限制其大小,同时提高其处理能力。在本文中,我们提出了两个人类检测模型,并介绍了它们的算法,以追踪相应的轨迹。我们描述了提出的模型,并将它们扩展到了解决该问题的挑战。我们的评估目标是确定模型的准确性,同时呈现它们在嵌入式设备中的需要。
URL
https://arxiv.org/abs/2303.11170