Image based estimation of real food size for accurate food calorie estimation
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Image based estimation of real food size for accurate food calorie estimation
- 1. Abstract: Obesity treatment requires obese patients to record all food intakes per day. Computer vision has been applied to estimate calories from food images. In order to increase detection accuracy and reduce the error of volume estimation in food calorie estimation, we present our calorie estimation method in this paper. To estimate calorie of food, a top view and side view are needed. Yolo technique is used to detect each food and calibration object. Finally we estimate each food’s calorie. And the experiment results show our estimation method is effective Existing System Obesity is a medical condition in which excess body fat has accumulated to the extent that it may have a negative effect on health. People are generally considered obese when their Body Mass Index (BMI). High BMI is associated with the increased risk of diseases, such as heart disease, type two diabetes, etc. Unfortunately, more and more people will meet criteria for obesity. The main cause of obesity is the imbalance between the amount of food intake and energy consumed by the individuals. While computer vision-based measurement methods were not applied to estimate calories from food images which includes calibration objects; obese patients have benefited a lot from these methods. Proposed System In this proposed system, we present our food calorie estimation results. Our method includes 5 steps: image acquisition, object detection, image segmentation volume estimation and calorie estimation. To estimate calories, it requires the user to take a top view and a side view of the food before eating with his/her machine. Each images used to estimate must include a calibration object; in our experiments, we use Yolo and Food100 dataset as a reference. SYSTEM CONFIGURATION: Hardware requirements: Processer : Any Update Processer Ram : Min 1 GB Hard Disk : Min 100 GB
- 2. Software requirements: Operating System : Windows family Technology : Python 3.6 IDE : PyCham UML : Star UML DFD : DFD Drawer Implemented by Development team : Cloud Technologies Website : http://www.cloudstechnologies.in Contact : 8121953811, 040-66411811