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Erschienen in:
2018 | OriginalPaper | Buchkapitel
AR DeepCalorieCam: An iOS App for Food Calorie Estimation with Augmented Reality
verfasst von : Ryosuke Tanno, Takumi Ege, Keiji Yanai
Erschienen in: MultiMedia Modeling
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Abstract
A food photo generally includes several kinds of food dishes. In order to recognize multiple dishes in a food photo, we need to detect each dish in a food image. Meanwhile, in recent years, the accuracy of object detection has improved drastically by the appearance of Convolutional Neural Network (CNN). In this demo, we present two automatic calorie estimation apps, DeepCalorieCam and AR DeepCalorieCam, running on iOS. DeepCalorieCam can estimate food calories by detecting dishes from the video stream captured from the built-in camera of an iPhone. We use YOLOv2 [1] which is the state-of-the-art object detector using CNN, as a dish detector to detect each dish in a food image, and the food calorie of each detected dish is estimated by image-based food calorie estimation [2, 3]. AR DeepCalorieCam is a combination of calorie estimation and augmented reality (AR) which is an AR version of DeepCalorieCam.