The role of film and television big data in real-time image detection and processing in the Internet of Things era

With the rapid development of the Internet, images play an increasingly critical role as an important data source in the Internet of Things (IoT). To meet the low-latency and high-efficiency transmission method of the IoT platform, the current problems in the image processing are analyzed from the perspective of real-time image processing in this study. The image features are extracted with the back propagation neural network (BPNN), and the images are classified with the support vector machines (SVM). A real-time image detection and processing platform (RT-IDPP) is constructed using the Adaboost framework based on the IoT, and the real-time image transmission and processing is realized based on different databases. It is found that the RT-IDPP proposed for the IoT realizes the image detection and tracking. The proposed method can not only run effectively on different cloud platforms for use, but also meet the real-time requirements in the image detection and tracking process, ensuring that the image detection rate is higher than 97%. Thus, the detection effect is better. Compared with the traditional image detection methods, the proposed method has higher detection rate and lower false-negative rate (FNR) and false-positive rate (FPR). The experimental detection effect on the film and television big data (FTBD) database is significantly better than that of other databases. This research can provide a theoretical basis for related researches on real-time image processing in the environment of IoT.