Abstract
With the development of the Internet of Things (IoT), the amount of global data is increasing rapidly. However, due to the increased burden of the cloud network, it is difficult to implement low-latency and high-efficiency video analysis in the cloud computing mode. To address this issue, this paper proposes an Intelligent Video Analysis System (IVAS) that can execute deep learning algorithms on low-power edge IoT devices, such as face detection and face recognition algorithms. IVAS enables fast, accurate, and real-time inference calculations of intelligent video analysis algorithms, providing an evaluation platform for performance testing, key parameter selection, and result analysis. The experiments based on real-world data confirm that IVAS can achieve good performance in people counting under an edge computing environment.