Deep learning based visual trackers entail offline pre-training on large volumes of video datasets with accurate bounding box annotations that are labor-expensive to achieve.
The ever-increasing data traffic, various delay-sensitive services, and the massive deployment of energy-limited Internet of Things (IoT) devices have brought huge challenges to the current communication networks, motivating academia and industry to move to the sixth-generation (6G) network.
Most top-ranked long-term trackers adopt the offline-trained Siamese architectures, thus, they cannot benefit from great progress of short-term trackers with online update.
In order to increase accuracy of detection and reduce the error of volume estimation in food calorie estimation, we present our calorie estimation method in this paper.
In this paper, we present a novel food image data set with volume and mass records of foods, and a deep learning method for food detection, to make a complete calorie estimation.