# FoodTracker: A Real-time Food Detection Mobile Application by Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1909.05994 · HTML: https://mlsanity.com/paper/1909.05994/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2019.

## Built on by (most cited first, 5 of 5)

- [A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment](https://mlsanity.com/paper/2106.11776/) (2021)
- [SeeDS: Semantic Separable Diffusion Synthesizer for Zero-shot Food Detection](https://mlsanity.com/paper/2310.04689/) (2023)
- [Object Detection in Indian Food Platters using Transfer Learning with YOLOv4](https://mlsanity.com/paper/2205.04841/) (2022)
- [NutrifyAI: An AI-Powered System for Real-Time Food Detection, Nutritional Analysis, and Personalized Meal Recommendations](https://mlsanity.com/paper/2408.10532/) (2024)
- [A Real-time Junk Food Recognition System based on Machine Learning](https://mlsanity.com/paper/2203.11836/) (2022)

## Abstract (arXiv)

We present a mobile application made to recognize food items of multi-object meal from a single image in real-time, and then return the nutrition facts with components and approximate amounts. Our work is organized in two parts. First, we build a deep convolutional neural network merging with YOLO, a state-of-the-art detection strategy, to achieve simultaneous multi-object recognition and localization with nearly 80% mean average precision. Second, we adapt our model into a mobile application with extending function for nutrition analysis. After inferring and decoding the model output in the app side, we present detection results that include bounding box position and class label in either real-time or local mode. Our model is well-suited for mobile devices with negligible inference time and small memory requirements with a deep learning algorithm.