Challenges in the Realm of Embedded Real-Time Image Processing

The development of power-efficient solutions gives new embedded products the ability to analyse images and thereby brings more intelligence to embedded systems—providing more and better services of higher quality as well as advanced capabilities such as self-adaptation and autonomy. This will allow cars to drive safer, medical devices to assist surgeons, and autonomous drones to find people that have gotten lost. For small-series products, one needs to find an embedded platform that provides enough performance, does not exceed the target price, and has sufficiently low-power consumption. As these requirements are typically conflicting, image processing engineers spend considerable time identifying the best possible trade-off for their algorithm implementation on the chosen platform. Providing a common platform that allows the efficient implementation of image processing systems across diverse application domains—a key objective of our TULIPP project—requires a solid understanding of the constraints and challenges of each domain. In this paper, we report the key challenges we identified within the medical, Unmanned Aerial Vehicle (UAV), and automotive domains to aid the community in developing the next generation of embedded image processing systems