Food Depth Estimation Using Low-Cost Mobile-Based System for Real-Time Dietary Assessment
Keywords:Depth estimation, 3D reconstruction, nutrition analysis, 2D segmentation, 3D triangulation, image segmentation
Real time estimation of nutrition intake from regular food items using mobile-based applications could be a breakthrough in creating public awareness of threats in overeating or faulty food choices. The bottleneck in implementing such systems is to effectively estimate the depths of the food items which is essential to calculate the volumes of foods. Volumes and density of food items can be used to estimate the weights of food eaten and their corresponding nutrition contents. Without specific depth sensors, it is very difficult to estimate the depth of any object from a single camera. Such sensors are equipped only in very advanced and expensive mobile devices. This work investigates the possibilities of using regular cameras to calculate the same using a specific frame structure. We proposed a controlled camera setup to acquire overlapping images of the food from different positions already calibrated to estimate the depths. The results were compared with the Kinect device’s depth measures to show the efficiency of the proposed method. We further investigated the optimum number of camera positions, their corresponding angles, and distances from the object to propose the best configuration for such a controlled system of image acquisition with regular mobile cameras. Overall the proposed method presents a low-cost solution to the depth estimation problem and opens up the possibilities for mobile-based apps for dietary assessment for various health-related problem-solving.
GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 6(1), Dec 2019 P 1-11
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