Deep Residual Network based food recognition for enhanced Augmented Reality application
This work addresses the need for healthier lifestyle choices by providing real-time food recognition in augmented reality, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of real-time food recognition for augmented reality applications by evaluating deep neural networks to identify the most suitable model for low-latency performance, achieving a system that delivers nutritional information to users.
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.