Computer Vision Based Food Recognition with Nutrition Analysis

Computer Vision Based Food Recognition with Nutrition Analysis

SHALINI K GOWDA, G S SUCHITHA, LAKSHMI S, DARSHAN GOWDA T S

Department of Computer Science and Engineering

 DAYANANDA SAGAR ACADEMY OF TECHNOLOGY AND MANAGEMENT

Udayapura, Kanakapura Road, Bangalore-560082

Email:shalinikg32@gmail.com

ABSTRACT

Tracking food intake can give insights into eating habits; hence it is useful to confront the rising public health threat of obesity and overweight. Because of high pace of modern life, people usually do not have time for manually recording their daily meals. Therefore, it is necessary to have an application which can accelerate the tracking process by automatically detecting foods from input image, collecting nutrition facts and recording. Deep learning methods have already proved promising in the food detection challenge. Among these methods, Tensor Flow Object Detection API is an open-source framework, consists of many pre-trained models that can be used to train and deploy a custom object detection model. Furthermore, there are some pre-trained models uses the Mobile Net which is an efficient con- volitional neural network for mobile vision applications