Challenges of Real-time Processing with Embedded Vision for IoT Applications
Suk Jin Lee · 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) · 2022
Recent advances in both Artificial Intelligent (AI) and the Internet of Things (loT) make it possible to implement surveillance systems that can detect and recognize objects in an automatic manner. It is still challenging to set up the embedded visions in resource-limited devices. This study investigates how the system configuration and the memory size affect the performance of embedded vision applications. For this proj ect, we set up a vision sensor accessible wirelessly from a single board computer (SBC). The designed system utilizes Raspberry Pi (SBC) as a computing server, and Python programming language to receive the captured images from the vision sensor using an open-source computer vision and machine learning software (OpenCV) library, which can detect objects and allow for easy accommodation with high performance. We also use a set of Python classes (Image ZM Q) that can transport live video streams from one controller to another for a distributed image processing network. We evaluate the performance with frame rate, frame transfer delay, and frame processing time. The proposed system is cost-efficient and suitable for home security, industrial wireless control, and other loT applications.