In recent years, the unmanned economy has begun to develop rapidly worldwide. Unmanned shops use technology to replace human personnel and increase efficiency. This technology has increased the demand for accurate and efficient camera surveillance systems. Camera handoff is a crucial step in the tasks of continuous target tracking and the maintenance of consistent crosscamera target marking in a multicamera surveillance system. In this study, we proposed a method of indoor multicamera handoff. To ensure continuous target tracking, the minimum number of required frames was maintained and at least one camera tracked each target. We employed the background subtraction approach to detect the target. Next, we used three trackability measures to evaluate the tracking object and trigger camera handoff accordingly, selecting the optimal camera for target tracking. The three measures considered were the resolution, occlusion, and distance to the edge of the camera's field of view. When one of these reached a preset threshold with an increasing trend, the system triggered the camera handoff.

With the limitation of air traffic and the rapid increase in the number of flights, flight delay is becoming more frequent. Flight delay leads to financial and time losses for passengers and increases operating costs for airlines. Therefore, the establishment of an accurate prediction model for flight delay becomes vital to build an efficient airline transportation system. The air transportation system has a huge amount of data and complex operation modes, which is suitable for analysis by using machine learning methods. This paper discusses the factors that may affect the flight delay, and presents a new flight delay prediction model. The five warning levels are defined based on flight delay database by using K-means clustering algorithm. After extracting the key factors related to flight operation by the grey relational analysis (GRA) algorithm, an improved machine learning algorithm called GRA — Genetic algorithm (GA) — back propagation neural network, GRA-GA-BP, is introduced, which is optimized by GA. The calculation results show that, compared with models before optimization and other two algorithms in previous papers, the proposed prediction model based on GRA-GA-BP algorithm shows a higher prediction accuracy and more stability. In terms of operation efficiency and memory consumption, it also has good performance. The analysis presented in this paper indicates that this model can provide effective early warnings for flight delay, and can help airlines to intervene in flights with abnormal trend in advance.