A method for embedding a computer vision application into a wearable device

Abstract

Pattern classification applications can be found everywhere, especially the ones that use computer vision. What makes them difficult to embed is the fact that they often require a lot of computational resources. Embedded computer vision has been applied in many contexts, such as industrial or home automation, robotics, and assistive technologies. This work performs a design space exploration in an image classification system and embeds a computer vision application into a minimum resource platform, targeting wearable devices. The feature extractor and the classifier are evaluated for memory usage and computation time. A method is proposed to optimize such characteristics, leading to a reduction of over 99% in computation time and 92% in memory usage, with respect to a standard implementation. Experimental results in an ARM Cortex-M platform showed a total classification time of 0.3 s, maintaining the same accuracy as in the simulation performed. Furthermore, less than 20 KB of data memory was required, which is the most limited resource available in low-cost and low-power microcontrollers. The target application, used for the experimental evaluation, is a crosswalk detector used to help visually impaired persons.