低分辨率红外感知系统的人体行为数据增强

To solve the problem of the lack of public human behavior datasets, two data augmentation methods are designed, based on generative adversarial networks and 3D human infrared models, to rapidly expand existing infrared human behavior datasets in this article. An improved generative network model AC-WGAN is formulated by adding network optimization strategy to generate high-quality infrared heat maps. The Unity 3D engine is used to build a mannequin with infrared features and motion information. By simulating the imaging principle of infrared array sensors, the function is realized to automatically generate a large and diverse amount of data for a given mannequin and sensor orientation information. A convolutional neural network is established, which is based on the data-enhanced dataset. Experimental results show that the perceptual accuracy of different behaviors is up to 80% . The ability of the network to identify unfamiliar data is significantly improved and the effectiveness of the designed data augmentation method is proven for expanding the human behavior infrared dataset.