AR DeepCalorieCam V2: Food Calorie Estimation with CNN and AR-based Actual Size Estimation

Ryosuke Tanno    Takumi Ege    Keiji Yanai

Department of Informatics, The University of Electro-Communication

ACM Symposium on Virtual Reality Software and Technology (VRST2018)

Ryosuke Tanno made the above..

Abstract

In most of the cases, the estimated calories are just associated with the estimated food categories, or the relative size compared to the standard size of each food category which are usually provided by a user manually. In addition, in the case of calorie estimation based on the amount of meal, a user conventionally needs to register a size-known reference object in advance and to take a food photo with the registered reference object. In this demo, we propose a new approach for food calorie estimation with CNN and Augmented Reality (AR)-based actual size estimation. By using Apple ARKit framework, we can measure the actual size of the meal area by acquiring the coordinates on the real world as a threedimensional vector, we implemented this demo app. As a result, it is possible to calculate the size more accurately than in the previous method by measuring the meal area directly, the calorie estimation accuracy has improved.

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Paper

PDF, 2018.

Citation

Ryosuke Tanno, Takumi Ege and Keiji Yanai. "AR DeepCalorieCam V2: Food Calorie Estimation with CNN and AR-based Actual Size Estimation", ACM Symposium on Virtual Reality Software and Technology (VRST), 2018. Bibtex







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