论文标题
SD测量：一个社会遥远的探测器
SD-Measure: A Social Distancing Detector
论文作者
论文摘要
社会距离的实践对于遏制传染性疾病的传播至关重要，并且在联盟19日大流行期间全球范围内被作为一种非药物预防措施。这项工作提出了一个新颖的框架，名为SD量表，用于检测与视频素材的社交距离。提出的框架利用面具R-CNN深神经网络来检测视频框架中的人。为了始终如一地确定在人们之间的互动过程中是否实践社会距离，质心跟踪算法被用来在镜头过程中跟踪主题。借助真实的算法，用于近似于人们与摄像机的距离以及他们之间的距离，我们确定是否遵守社会疏远指南。在自定义视频录像数据集（CVFD）和自定义的个人图像数据集（CPID）测试时，该框架与较低的错误警报率相结合，并具有较低的错误警报率，在该数据集（CPID）中，它在确定是否实践了社交距离指南方面表现出其有效性。
The practice of social distancing is imperative to curbing the spread of contagious diseases and has been globally adopted as a non-pharmaceutical prevention measure during the COVID-19 pandemic. This work proposes a novel framework named SD-Measure for detecting social distancing from video footages. The proposed framework leverages the Mask R-CNN deep neural network to detect people in a video frame. To consistently identify whether social distancing is practiced during the interaction between people, a centroid tracking algorithm is utilised to track the subjects over the course of the footage. With the aid of authentic algorithms for approximating the distance of people from the camera and between themselves, we determine whether the social distancing guidelines are being adhered to. The framework attained a high accuracy value in conjunction with a low false alarm rate when tested on Custom Video Footage Dataset (CVFD) and Custom Personal Images Dataset (CPID), where it manifested its effectiveness in determining whether social distancing guidelines were practiced.
