Implementing a Real-Time Image Captioning Service for Scene Identification Using Embedded System

This work aims to implement a real-time scene identification system using an image captioning model on an embedded system. The image captioning model can translate the image captured by a webcam installed on the embedded system into a human-readable sentence immediately. Users can get the information quickly by reading only the sentences. There are two stages in the image captioning model. First, a deep neural network extracts features from images captured from the webcam. Second, a long-short term memory generates the corresponding sentence. Due to the portability of the embedded system, our scene identification system can be placed anywhere at home or in the company. We evaluate the execution time in different aspects on several embedded systems and demonstrate the generated sentences from the captured images by our scene identification system.