Testbeds and Research Infrastructures for the Development of Networks and Communications. 14th EAI International Conference, TridentCom 2019, Changsha, China, December 7-8, 2019, Proceedings

Research Article

Food Recognition and Dietary Assessment for Healthcare System at Mobile Device End Using Mask R-CNN

  • @INPROCEEDINGS{10.1007/978-3-030-43215-7_2,
        author={Hui Ye and Qiming Zou},
        title={Food Recognition and Dietary Assessment for Healthcare System at Mobile Device End Using Mask R-CNN},
        proceedings={Testbeds and Research Infrastructures for the Development of Networks and Communications. 14th EAI International Conference, TridentCom 2019, Changsha, China, December 7-8, 2019, Proceedings},
        proceedings_a={TRIDENTCOM},
        year={2020},
        month={3},
        keywords={Food image processing Dietary monitoring Mobile terminal recognition},
        doi={10.1007/978-3-030-43215-7_2}
    }
    
  • Hui Ye
    Qiming Zou
    Year: 2020
    Food Recognition and Dietary Assessment for Healthcare System at Mobile Device End Using Mask R-CNN
    TRIDENTCOM
    Springer
    DOI: 10.1007/978-3-030-43215-7_2
Hui Ye1, Qiming Zou1,*
  • 1: Shanghai University
*Contact email: kim@shu.edu.cn

Abstract

Monitoring and estimation of food intake is of great significance to health-related research, such as obesity management. Traditional dietary records are performed in manual way. These methods are of low efficiency and a waste of labor, which are highly dependent on human interaction. In recent years, some researches have made progress in the estimation of food intake by using the computer vision technology. However, the recognition results of these researches are usually for the whole food object in the image, and the accuracy is not high. In terms of this problem, we provide a method to the food smart recognition and automatic dietary assessment on the mobile device. First, the food image is processed by MASK R-CNN which is more efficient than traditional methods. And more accurate recognition, classification and segmentation results of the multiple food items are output. Second, the OpenCV is used to display the food category and the corresponding food information of unit volume on the recognition page. Finally, in order to facilitate daily use, TensorFlow Lite is used to process the model to transplant to the mobile device, which can help to monitor people’s dietary intake.