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.

随着Internet的快速发展，图像作为物联网（IoT）中的重要数据源，发挥着越来越重要的作用。为了满足物联网平台的低延迟，高效率的传输方式，本文从实时图像处理的角度分析了当前图像处理中存在的问题。利用反向传播神经网络（BPNN）提取图像特征，并利用支持向量机（SVM）对图像进行分类。利用基于物联网的Adaboost框架构建了实时图像检测与处理平台（RT-IDPP），并基于不同的数据库实现了实时图像的传输与处理。结果发现，针对物联网提出的RT-IDPP实现了图像检测和跟踪。该方法不仅可以在不同的云平台上有效运行，而且可以满足图像检测和跟踪过程中的实时性要求，确保图像检测率高于97％。因此，检测效果更好。与传统的图像检测方法相比，该方法具有较高的检测率和较低的假阴性率（FNR）和假阳性率（FPR）。影视大数据（FTBD）数据库的实验检测效果明显优于其他数据库。该研究可以为物联网环境下实时图像处理的相关研究提供理论依据。确保图像检测率高于97％。因此，检测效果更好。与传统的图像检测方法相比，该方法具有较高的检测率和较低的假阴性率（FNR）和假阳性率（FPR）。影视大数据（FTBD）数据库的实验检测效果明显优于其他数据库。该研究可以为物联网环境下实时图像处理的相关研究提供理论依据。确保图像检测率高于97％。因此，检测效果更好。与传统的图像检测方法相比，该方法具有较高的检测率和较低的假阴性率（FNR）和假阳性率（FPR）。影视大数据（FTBD）数据库的实验检测效果明显优于其他数据库。该研究可以为物联网环境下实时图像处理的相关研究提供理论依据。影视大数据（FTBD）数据库的实验检测效果明显优于其他数据库。该研究可以为物联网环境下实时图像处理的相关研究提供理论依据。影视大数据（FTBD）数据库的实验检测效果明显优于其他数据库。该研究可以为物联网环境下实时图像处理的相关研究提供理论依据。