arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.04292cs.HCcs.CV
收藏

基于深度残差网络的增强增强现实食物识别

Deep Residual Network based food recognition for enhanced Augmented Reality application

Vellore institute of Technology(韦洛尔理工大学)

Siddarth S, Sainath G, Vignesh S

摘要:

基于深度神经网络的学习方法被广泛用于基于图像分类或目标检测的问题,具有显著的效果。对象的实时对象状态估计可以用于跟踪和估计当前帧的对象所具有的特征,而不会引起任何显著的延迟和误分类。可以从相机图像检测当前状态下的这种对象的特征的系统可以用于增强增强现实的应用,以改善用户体验并以更感性的方式传递信息。本文的重点是确定最合适的模型来创建低延迟辅助AR,通过向用户提供有关他们所消费食物的营养信息来帮助他们,以促进更健康的生活选择。因此,数据集是以这种方式收集和获取的,我们进行了各种测试,以确定在性能和复杂性方面最合适的DNN,并建立一个系统,将这些信息实时呈现给用户。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑