Dataset Construction and Real-Time Monitoring for Developing an Intelligent Drowning Warning Algorithm by the CamTra System

To solve the problem of sparse images in real-world drowning datasets, this study aims to create an intelligent system that can generate a large number of drowning datasets by optimizing AI image generation algorithms. The system will gradually be used to make up for the shortage of rare real-world drowning datasets based on the CamTra (camera tracking) System. This method is not only based on traditional AI image generation steps but also optimizes the engine framework to create more drowning datasets. For the key elements of drowning, on the one hand, different filters, especially blue and green filters, will be added to distinguish color differences between underwater and above water. On the other hand, the framework structure of the generative adversarial network (GAN), variational autoencoder (VAE), and diffusion model will be optimized to further reduce system computation. Meanwhile, the detection of drowning swimmers in the system will become clearer. It can greatly improve the performance and efficiency of drowning monitoring algorithms. The artificially generated drowning dataset generated by AI can describe different real-world drowning processes and perfectly adapt to different emergencies. This method is also applicable to dangerous behaviors that are difficult to record.