Feature-Centric Video Transmission and Analytics in Large-Scale Internet of Video Things

The interconnection of large-scale visual sensors is called the Internet of Video Things (IoVT), which brings a qualitative leap to the interaction of urban information. However, communication delay and resource allocation have brought challenges to the development of IoVT. In this paper, we propose a novel city surveillance IoVT architecture to improve performance. This paradigm consists of front-end target region capture, edge computing and cloud-end feature matching, which can adapt the channel and computing resource allocation ratio flexibly, avoiding communication link congestion caused by unnecessary video uploading. Simulation results show that the proposed scheme is feasible, and can realize efficient data transmission and analysis in an IoVT-based smart city.