Browsing by Subject "food weight estimation"

In this thesis we investigate the feasibility of machine learning methods for estimating the type and the weight of individual food items from images taken of customers’ plates at a buffet- style restaurant. The images were collected in collaboration with the University of Turku and Flavoria, a public lunch-line restaurant, where a camera was mounted above the cashier to automatically take a photo of the foods chosen by the customer when they went to pay. For each image, an existing system of scales at the restaurant provided the weights for each individual food item. We describe suitable model architectures and training setups for the weight estimation and food identification tasks and explain the models’ theoretical background. Furthermore we propose and compare two methods for utilizing a restaurant’s daily menu information for improving model performance in both tasks. We show that the models perform well in comparison to baseline methods and reach accuracy on par with other similar work. Additionally, as the images were captured automatically, in some of the images the food was occluded or blurry, or the image contained sensitive customer information. To address this we present computer vision techniques for preprocessing and filtering the images. We publish the dataset containing the preprocessed images along with the corresponding individual food weights for use in future research. The main results of the project have been published as a peer-reviewed article in the International Conference in Pattern Recognition Systems 2022. The article received the best paper award of the conference.