论文标题
深度剩余网络的食物识别，以增强增强现实应用
Deep Residual Network based food recognition for enhanced Augmented Reality application
论文作者
论文摘要
基于神经网络的深度学习方法被广泛用于图像分类或基于对象检测的问题，具有显着的结果。实时对象状态对象的估计可用于跟踪和估计当前框架对象所具有的特征，而不会引起任何明显的延迟和错误分类。可以使用相机图像检测到当前状态中此类对象的功能的系统，可用于增强增强现实的应用，以改善用户体验并以多种感知的方式传递信息。本文背后的重点是确定最合适的模型，以创建低延迟援助AR来帮助用户提供有关他们食用的食物以促进更健康的生活选择的营养信息。因此，数据集是以这样的方式收集和获取的，我们进行了各种测试，以确定性能和复杂性方面最合适的DNN，并建立一个将此类信息实时的系统。
Deep neural network based learning approaches is widely utilized for image classification or object detection based problems with remarkable outcomes. Realtime Object state estimation of objects can be used to track and estimate the features that the object of the current frame possesses without causing any significant delay and misclassification. A system that can detect the features of such objects in the present state from camera images can be used to enhance the application of Augmented Reality for improving user experience and delivering information in a much perceptual way. The focus behind this paper is to determine the most suitable model to create a low-latency assistance AR to aid users by providing them nutritional information about the food that they consume in order to promote healthier life choices. Hence the dataset has been collected and acquired in such a manner, and we conduct various tests in order to identify the most suitable DNN in terms of performance and complexity and establish a system that renders such information realtime to the user.
